Systems and methods for monitoring of mechanical and electrical machines

ABSTRACT

A system for continuously monitoring at least one machine including a plurality of magnetic sensors synchronously sensing magnetic fields emitted by at least one machine, the plurality of magnetic sensors sensing the magnetic fields along a corresponding plurality of channels and outputting magnetic field emission signals corresponding to the magnetic fields, a signal analyzer receiving at least a portion of the magnetic field emission signals and performing analysis of the magnetic field emission signals, the signal analyzer providing an output based on the analysis, the output including at least an indication of a condition of the at least one machine and a control module receiving the indication of the condition and initiating at least one of a repair event on the at least one machine, an adjustment to a maintenance schedule of the at least one machine and an adjustment to an operating parameter of the at least one machine based on the indication, whereby efficacy of the at least one machine is improved.

RELATED APPLICATIONS

Reference is hereby made to U.S. Provisional Patent Application No.62/490,108 entitled MAGNETIC ANALYSIS OF ELECTRICAL MOTORS, filed Apr.26, 2017; to U.S. Provisional Patent Application No. 62/503,984 entitledCONTINUOUS MONITORING AND DIAGNOSIS TOOL FOR MECHANICAL SYSTEMS, filedMay 10, 2017; to U.S. Provisional Patent Application No. 62/579,348,entitled AUTOMATED SYSTEMS AND METHODS FOR MONITORING ELECTRICALMACHINES USING MAGNETIC FIELDS MEASUREMENTS, filed Oct. 31, 2017; and toU.S. Provisional Patent Application No. 62/579,356, entitled AUTOMATEDMONITORING AND DIAGNOSIS OF MECHANICAL SYSTEMS, filed Oct. 31, 2017, thedisclosures of all of which are hereby incorporated by reference andpriorities of all of which are hereby claimed pursuant to 37 CFR1.78(a)(4) and (5)(i).

Reference is also made to U.S. Pat. No. 9,835,594 entitled AUTOMATICMECHANICAL SYSTEM DIAGNOSIS, filed Oct. 22, 2012, the disclosure ofwhich is hereby incorporated by reference.

FIELD OF THE INVENTION

The present application relates generally to systems and methods formonitoring machines, including mechanical and electrical machines, andmore particularly to the detection of problems in mechanical andelectrical machines based on such monitoring.

BACKGROUND OF THE INVENTION

Various types of systems for monitoring mechanical and electricalmachines are known in the art.

SUMMARY OF THE INVENTION

The present invention seeks to provide novel systems and methods formonitoring operation of mechanical and electrical machines and for thedetection and prediction of problems in such machines based on themonitoring thereof.

There is thus provided in accordance with a preferred embodiment of thepresent invention a system for continuously monitoring at least onemachine including a plurality of magnetic sensors synchronously sensingmagnetic fields emitted by at least one machine, the plurality ofmagnetic sensors sensing the magnetic fields along a correspondingplurality of channels and outputting magnetic field emission signalscorresponding to the magnetic fields, a signal analyzer receiving atleast a portion of the magnetic field emission signals and performinganalysis of the magnetic field emission signals, the signal analyzerproviding an output based on the analysis, the output including at leastan indication of a condition of the at least one machine and a controlmodule receiving the indication of the condition and initiating at leastone of a repair event on the at least one machine, an adjustment to amaintenance schedule of the at least one machine and an adjustment to anoperating parameter of the at least one machine based on the indication,whereby efficacy of the at least one machine is improved.

Preferably, the system also includes at least one vibration sensorsensing vibrations arising from the at least one machine and outputtingvibration signals corresponding to the vibrations, the sensing of thevibrations being performed synchronously with the sensing of themagnetic fields, the signal analyzer receiving at least a portion of thevibration signals.

Preferably, the analysis includes phase analysis of phases at least ofthe magnetic field emission signals.

Preferably, the analysis includes machine-learning functionality.

Preferably, the signal analyzer includes at least one data processingmodule in communication with at least one of the plurality of magneticsensors and a cloud processing server in communication with the at leastone data processing module.

Preferably, the system also includes a low-power consumption sensorhaving a power uptake of less than or equal to 1 microwatt, forcontinuously sensing at least one operational parameter of the at leastone machine.

Preferably, the low-power consumption sensor is operatively coupled toat least one of the plurality of magnetic sensors for automaticallycontrolling operation of the at least one of the plurality of magneticsensors based on the operational parameter.

Preferably, the at least one machine includes at least one of anelectrical machine and a mechanical machine.

Preferably, the electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the electrical machine includes at least one of a motor anda generator.

There is also provided in accordance with another preferred embodimentof the present invention a system for continuously monitoring at leastone machine including at least one magnetic sensor sensing magneticfield emission arising from at least one machine and outputting magneticfield emission signals corresponding to the magnetic field emission, atleast one vibration sensor sensing vibrations arising from the at leastone machine and outputting vibration signals corresponding to thevibrations, the sensing of the vibrations being performed synchronouslywith the sensing of the magnetic field emission, a signal analyzerreceiving at least a portion of the magnetic field emission signals andthe vibration signals and performing analysis of the magnetic fieldemission signals with respect to the vibration signals, the signalanalyzer providing an output based on the analysis, the output includingat least an indication of a condition of the at least one machine and acontrol module receiving the indication of the condition and initiatingat least one of a repair event on the at least one machine, anadjustment to a maintenance schedule of the at least one machine and anadjustment to an operating parameter of the at least one machine basedon the indication, whereby efficacy of the at least one machine isimproved.

Preferably, the analysis includes phase analysis of phases of themagnetic field emission signals and the vibration signals.

Preferably, the analysis includes machine-learning functionality.

Preferably, the signal analyzer includes at least one data processingmodule in communication with the at least one magnetic sensor andvibration sensor and a cloud processing server in communication with theat least one data processing module.

Preferably, the system also includes a low-power consumption sensorhaving a power uptake of less than or equal to 1 microwatt, forcontinuously sensing at least one operational parameter of the at leastone machine.

Preferably, the low-power consumption sensor is operatively coupled tothe at least one magnetic sensor and vibration sensor for automaticallycontrolling operation thereof based on the operational parameter.

Preferably, the automatically controlling includes adjusting a samplingperiodicity of at least one of the magnetic and vibration sensor.

Preferably, the at least one machine includes at least one of anelectrical machine and a mechanical machine.

Preferably, the electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the electrical machine includes at least one of a motor anda generator.

There is further provided in accordance with an additional preferredembodiment of the present invention a system for continuously monitoringat least one machine including a low-power sensor having a power uptakeof less than or equal to 1 microwatt at least near continuouslymonitoring an operating parameter of at least one machine and outputtingsignals corresponding to the operating parameter, a signal analyzerreceiving at least a portion of the signals and providing an outputindication of a condition of the at least one machine based on analysisof the signals and at least one additional sensor cooperatively coupledto the low-power sensor, operation of the at least one additional sensorbeing initiated based on the condition.

Preferably, the low-power sensor includes a vibration sensor and theoperating parameter includes vibrations.

Preferably, the condition includes an on condition or an off condition.

Additionally or alternatively, the condition includes a properlyoperating or improperly operating condition.

Preferably, the improperly operating condition includes one of an actualor impending faulty condition.

Preferably, the additional sensor includes a sensor having a poweruptake greater than the power uptake of the low-power sensor.

Preferably, the additional sensor includes at least one operatingparameter sensor for sensing at least one additional operating parameterof the machine.

Preferably, the additional operating parameter is not the same operatingparameter as the operating parameter sensed by the low-power sensor.

Preferably, the additional sensor includes at least one of a magneticsensor and a vibration sensor.

Preferably, the additional sensor includes at least one magnetic sensorand at least one vibration sensor operating mutually synchronously.

There is still further provided in accordance with another preferredembodiment of the present invention a system for continuously monitoringat least one machine including a low-power sensor at least nearcontinuously monitoring an operating parameter of at least one machineand outputting signals corresponding to the operating parameter, asignal analyzer receiving at least a portion of the signals andproviding an indication of exceedance by the signals of a predeterminedthreshold and at least one high-power sensor having a power uptakegreater than a power uptake of the low-power sensor and cooperativelycoupled to the low-power sensor, operation of the at least onehigh-power sensor being initiated based on the indication of exceedanceof the predetermined threshold.

Preferably, the low-power sensor includes a vibration sensor and theoperating parameter includes vibrations.

Preferably, the high-power sensor includes at least one of a magneticsensor and a vibration sensor.

Preferably, the high-power sensor includes at least one magnetic sensorand at least one vibration sensor operating mutually synchronously.

Preferably, the signal analyzer includes a CPU coupled to the low-powersensor.

Preferably, the system also includes circuitry connecting the CPU to theat least one high-power sensor for initiating operation of the at leastone high-power sensor.

Preferably, the signal analyzer includes a cloud-based signal analyzer.

Preferably, the predetermined threshold is set based on machinelearning.

Preferably, the low-power sensor has a power uptake of less than orequal to one microwatt.

Preferably, the low-power sensor monitors the operating parameter at asampling rate of at least six times per second.

There is yet further provided in accordance with still another preferredembodiment of the present invention a system for continuously monitoringat least one machine including at least one sensor monitoring at leastone operational parameter of at least one machine with a samplingperiodicity and providing at least one output signal corresponding tothe at least one operational parameter, a signal analyzer receiving atleast a portion of the at least one output signal and performinganalysis of the at least one output signal, the signal analyzerproviding an output indication of at least one of a condition of the atleast one machine and a condition of the at least one sensor based onthe analysis and a control module receiving the output indication andadjusting the sampling periodicity in at least near real time basedthereon.

Preferably, the condition of the machine includes an on condition or anoff condition.

Additionally or alternatively, the condition of the machine includes aproperly operating or improperly operating condition.

Additionally or alternatively, the condition of the machine includes oneof an actual or impending faulty condition.

Preferably, the condition of the at least one sensor includes a measureof remaining useful life (RUL) of the sensor.

Preferably, the RUL of the sensor includes a measure of remainingbattery life of the sensor.

Preferably, the signal analyzer includes a CPU coupled to the sensor.

Additionally or alternatively, the signal analyzer includes acloud-based signal analyzer.

Preferably, the analysis includes machine-learning functionality.

Preferably, the analysis takes into account a maintenance schedule ofthe at least one machine.

There is also provided in accordance with yet another preferredembodiment of the present invention a system for maintenance of at leastone electrical machine having at least one shared characteristic with aplurality of electrical machines, the system including, a plurality ofmagnetic sensors coupled to a corresponding plurality of electricalmachines having at least one shared characteristic for sensing magneticfields generated thereby, the plurality of magnetic sensors providingoutput indications of the magnetic fields of the corresponding pluralityof electrical machines, correlating functionality receiving the outputindications of the magnetic fields of the corresponding plurality ofelectrical machines and providing a correlation output indication of acorrelation between the magnetic fields and past failures ofcorresponding ones of the plurality of electrical machines, at least onemagnetic sensor associated with a given electrical machine having the atleast one shared characteristic for providing an individual outputindication of magnetic fields generated by the given electrical machine,predicting functionality receiving the correlation output indication andthe individual output indication and providing a predictive outputindication at least of time to failure of the given electrical machine,based on applying the correlation output indication to the individualoutput indication and a notification module providing a human-sensiblenotification including at least the predictive output indication, atleast one of control, repair or maintenance activities being performedupon the given electrical machine in accordance with the notification.

Preferably, the electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the given electrical machine includes an electricalgenerator.

Preferably, the given electrical machine includes an electrical motor.

Preferably, the given electrical machine includes at least one of an ACor DC electrical machine.

Preferably, the correlating functionality and the predictingfunctionality include machine learning functionality.

Preferably, the shared characteristic includes at least one of a sharedmechanical characteristic, shared electrical characteristic, sharedenvironmental characteristic and shared performance characteristic.

Preferably, the plurality of electrical machines includes the givenelectrical machine.

Alternatively, the plurality of electrical machines does not include thegiven electrical machine.

Preferably, the predictive output indication includes an indication ofan impending fault, the impending fault including at least one of acrawling fault, eccentricity, a damaged rotor bar, a stator short,electrical discharge, mechanical imbalance, energy loss, negative phasesequence and faults arising from extremum operating conditions.

There is further provided in accordance with another preferredembodiment of the present invention a system for automaticallyalleviating problematic conditions in electrical machines due tohacking, the system including a plurality of magnetic field sensorsassociated with a plurality of electrical machines having at least oneshared characteristic, the plurality of magnetic field sensors providinghistorical output indications of magnetic fields generated by theplurality of electrical machines, a correlator for correlating themagnetic fields generated by ones of the plurality of electricalmachines to at least one operational parameter in ones of the pluralityof electrical machines and providing a correlation output indication, amagnetic field sensor associated with a given electrical machine havingthe at least one shared characteristic for providing an individualoutput indication of magnetic fields generated by the given electricalmachine and a control output generator operative to receive thecorrelation output indication and the individual output indication forproviding a hacking responsive control output to the given electricalmachine based on a dissimilarity between the correlation outputindication and at least one of the individual output indication and theat least one operational parameter of the given electrical machine.

Preferably, the given electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the given electrical machine includes an electricalgenerator.

Alternatively, the given electrical machine includes an electricalmotor.

Preferably, the given electrical machine includes at least one of an ACor DC electrical machine.

Preferably, the correlating functionality and the predictingfunctionality include machine learning functionality.

Preferably, the shared characteristic includes at least one of a sharedmechanical characteristic, shared electrical characteristic, sharedenvironmental characteristic and shared performance characteristic.

Preferably, the plurality of electrical machines includes the givenelectrical machine.

Alternatively, the plurality of electrical machines does not include thegiven electrical machine.

Preferably, the predictive output indication includes an indication ofan impending fault, the impending fault including at least one of acrawling fault, eccentricity, a damaged rotor bar, a stator short,electrical discharge, mechanical imbalance, energy loss, negative phasesequence and faults arising from extremum operating conditions.

There is also provided in accordance with another preferred embodimentof the present invention a system for identifying potential failures andproviding pre-failure alerts for at least one machine having at leastone shared feature with a plurality of machines, the system including aplurality of operational parameter sensing modules associated with aplurality of machines having at least one common feature, the pluralityof operational parameter sensing modules providing historical outputindications of at least changes over time in at least one operationalparameter of each of the plurality of machines, a correlator operativeto correlate patterns of changes in the at least one operationalparameter in ones of the plurality of machines to past failures incorresponding ones of the plurality of machines and to provide acorrelation output indication, an operational parameter sensing moduleassociated with a given machine having the at least one common featurefor providing an individual output indication of at least a change overtime in the at least one operational parameter of the given machine, apredictor operative to receive the correlation output indication and theindividual output indication for providing a predictive outputindication of an impending failure in the given machine, based on asimilarity between the change over time in the at least one operationalparameter of the given machine indicated by the individual outputindication and the patterns of changes over time in the at least oneoperational parameter in the plurality of machines indicated by thehistorical output indications and a notification module providing anotification of a status of the given machine based on the predictiveoutput indication, at least one of control, repair or maintenanceactivities being performed upon the given machine in accordance with thenotification.

Preferably, the system also includes an audio playback module operativeto playback an audio signal having at least one characteristiccorresponding to the predictive output indication of an impendingfailure.

Preferably, the audio playback module is operative to selectivelyenhance the at least one characteristic of the audio signalcorresponding to the predictive output indication.

Preferably, the plurality of machines includes at least one ofelectrical machines and mechanical machines.

Preferably, the given machine includes an electrical motor.

Alternatively, the given machine includes a generator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

There is additionally provided in accordance with another preferredembodiment of the present invention a system for optimizing operation ofmachines, the system including a plurality of operational parametersensing modules associated with a plurality of machines having at leastone common feature, the plurality of operational parameter sensingmodules providing historical output indications of at least oneoperational parameter of each of the plurality of machines over time, acorrelator operative to correlate the at least one operational parameterin ones of the plurality of machines to at least one optimization metricof corresponding ones of the plurality of machines and providing acorrelator output, an operational parameter sensing module associatedwith a given machine having the at least one common feature forproviding an individual output indication of the at least oneoperational parameter of the given machine and a control outputgenerator operative to receive the correlator output and the individualoutput indication, for providing a control output useful for enablingthe given machine to operate in accordance with an operational parameterwhich is correlated by the correlator output to a desired optimizationmetric.

Preferably, the plurality of machines includes at least one ofelectrical machines and mechanical machines.

Preferably, the given machine includes an electrical motor.

Alternatively, the given machine includes a generator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

Preferably, the optimization metric includes at least one of machineefficiency, machine power consumption and machine vibration levels.

Preferably, the at least one optimization metric is obtained from anexternal source.

There is also provided in accordance with another preferred embodimentof the present invention q system for automatically alleviatingproblematic conditions in machine systems, the system including at leastone operational parameter sensing module providing historical outputindications of at least one operational parameter of at least onecomponent in a machine system, a correlator for correlating thehistorical output indications of the at least one operational parameterto historical indications of at least one parameter associated with atleast one other component in the machine system and providing acorrelation output indication, an individual operational parametersensing module associated with a given component in a given machinesystem, the given component having at least one feature in common withthe at least one component, for providing an individual outputindication of the at least one operational parameter of the givencomponent and a control output generator operative to receive thecorrelation output indication and the individual output indication, forapplying the correlation output indication to the individual outputindication and deriving the at least one parameter associated with atleast one other given component in the given system having at least onefeature in common with the at least one other component, and providing acontrol output to the given system based on the at least one parameterderived.

Preferably, the component in the machine system is an electrical device.

Additionally or alternatively, the component in the machine system is amechanical device.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

Preferably, the operating parameters of the other given component arenot directly sensed.

Preferably, the given component is cooperatively coupled to the othergiven component in the machine system.

Preferably, the given component is a pump and the other given componentis a chiller.

There is also provided in accordance with another preferred embodimentof the present invention a system for automatically sensing problematicconditions in machine systems due to malicious intervention therewith,the system including a plurality of operational parameter sensingmodules associated with a plurality of machine systems having at leastone common feature, the plurality of operational parameter sensingmodules providing historical output indications of at least oneoperational parameter of each of the plurality of machine systems, acorrelator operative to correlate the at least one operational parameterin ones of the plurality of machine systems to at least one otherparameter in ones of the plurality of machine systems and to provide acorrelation output indication, a parameter sensing module associatedwith a given machine system having the at least one common feature forproviding an individual output indication of at least one of theoperational parameter and the other parameter of the given machinesystem and an anomaly alert generator operative to receive thecorrelation output indication and the individual output indication forproviding an anomaly alert based on a dissimilarity between at least oneof the operational parameter and the other parameter of the givenmachine indicated by the individual output indication and at least oneof the operational parameter and the other parameter indicated by thehistorical output indications.

Preferably, the plurality of machine systems includes at least one ofelectrical machine systems and mechanical machine systems.

Preferably, the given machine system includes an electrical motor.

Additionally or alternatively, the given machine system includes agenerator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

There is also provided in accordance with still another preferredembodiment of the present invention a system for identifying potentialfailures and providing pre-failure alerts for at least one machinehaving at least one shared feature with a plurality of machines, thesystem including a plurality of magnetic field sensing modules at leastnear continuously sensing magnetic fields arising from a plurality ofmachines having at least one common feature, the plurality of magneticfield sensing modules providing historical output indications of atleast changes over time in a magnetic fields of each of the plurality ofmachines, a correlator operative to correlate patterns of changes in themagnetic fields in ones of the plurality of machines to past failures incorresponding ones of the plurality of machines and to provide acorrelation output indication, a plurality of magnetic field sensorsassociated with a given machine having the at least one common featurefor synchronously providing a plurality of individual output indicationsof at least a change over time in the magnetic fields of the givenmachine, a predictor operative to receive the correlation outputindication and the plurality of individual output indications forproviding a predictive output indication of an impending failure in thegiven machine, based on a similarity between the change over time in themagnetic fields of the given machine indicated by the plurality ofindividual output indications and the patterns of changes over time inthe magnetic fields in the plurality of machines indicated by thehistorical output indications and a notification module providing anotification of a status of the given machine based on the predictiveoutput indication, at least one of control, repair or maintenanceactivities being performed upon the given machine in accordance with thenotification.

There is further provided in accordance with an additional preferredembodiment of the present invention a system for identifying potentialfailures and providing pre-failure alerts for at least one machinehaving at least one shared feature with a plurality of machines, thesystem including a plurality of vibration sensing modules at least nearcontinuously sensing vibrations arising from a plurality of machineshaving at least one common feature, the plurality of vibration sensingmodules providing historical output indications of at least changes overtime in vibrations of each of the plurality of machines, a plurality ofmagnetic field sensing modules at least near continuously sensingmagnetic fields arising from the plurality of machines having the atleast one common feature, the sensing of the magnetic fields beingperformed synchronously with the sensing of the vibrations, theplurality of magnetic field sensing modules providing historical outputindications of at least changes over time in the magnetic fields of eachof the plurality of machines, a correlator operative to correlatepatterns of changes in the vibrations with respect to the magneticfields in ones of the plurality of machines to past failures incorresponding ones of the plurality of machines and to provide acorrelation output indication, at least one magnetic field sensorassociated with a given machine having the at least one common featurefor near continuously providing at least one individual outputindication of at least a change over time in the magnetic fields of thegiven machine, at least one vibration sensor associated with the givenmachine for near continuously providing, synchronously with the nearcontinuously providing by the at least one magnetic field sensor, atleast one individual output indication of at least a change over time inthe vibrations of the given machine, a predictor operative to receivethe correlation output indication and the indications of changes overtime of the magnetic fields and vibrations, for providing a predictiveoutput indication of an impending failure in the given machine, based ona similarity between the changes over time in the magnetic fields andvibrations of the given machine and the patterns of changes over time inthe magnetic fields and vibrations in the plurality of machinesindicated by the historical output indications and a notification moduleproviding a notification of a status of the given machine based on thepredictive output indication, at least one of control, repair ormaintenance activities being performed upon the given machine inaccordance with the notification.

There is also provided in accordance with another preferred embodimentof the present invention a system for optimizing operation of machines,the system including a plurality of magnetic sensor modules associatedwith a plurality of machines having at least one common feature, each ofthe plurality of magnetic sensor modules synchronously sensing magneticfields along at least two signal channels and providing historicaloutput indications of magnetic fields along the at least two signalchannels arising from each of the plurality of machines over time, acorrelator operative to correlate the historical output indications ofthe magnetic fields in ones of the plurality of machines to at least oneoptimization metric of corresponding ones of the plurality of machinesand to provide a correlator output, a magnetic sensor module associatedwith a given machine having the at least one common feature forproviding an individual output indication of magnetic fields arisingfrom the given machine, the magnetic sensor module synchronously sensingmagnetic fields along at least two signal channels and a control outputgenerator operative to receive the correlator output and the individualoutput indication, for providing a control output useful for enablingthe given machine to operate in accordance with an operational parameterwhich is correlated by the correlator output to a desired optimizationmetric.

There is additionally provided in accordance with another preferredembodiment of the present invention a method for continuously monitoringat least one machine including continuously synchronously sensingmagnetic fields emitted by at least one machine along a plurality ofchannels and outputting magnetic field emission signals corresponding tothe magnetic fields, receiving at least a portion of the magnetic fieldemission signals, performing analysis of the magnetic field emissionsignals and providing an output based on the analysis, the outputincluding at least an indication of a condition of the at least onemachine and receiving the indication of the condition and initiating atleast one of a repair event on the at least one machine, an adjustmentto a maintenance schedule of the at least one machine and an adjustmentto an operating parameter of the at least one machine based on theindication, whereby efficacy of the at least one machine is improved.

Preferably, the method also includes sensing vibrations arising from theat least one machine and outputting vibration signals corresponding tothe vibrations, the sensing of the vibrations being performedsynchronously with the sensing of the magnetic fields.

Preferably, the analysis includes phase analysis of phases at least ofthe magnetic field emission signals.

Preferably, the analysis includes machine-learning functionality.

Preferably, the analysis is performed by at least one data processingmodule and a cloud processing server in communication with the at leastone data processing module.

Preferably, the method also includes continuously sensing at least oneoperational parameter of the at least one machine by a low-powerconsumption sensor having a power uptake of less than or equal to 1microwatt.

Preferably, the low-power consumption sensor is operatively coupled toat least one magnetic sensor for automatically controlling operation ofthe at least one magnetic sensor based on the operational parameter.

Preferably, the at least one machine includes at least one of anelectrical machine and a mechanical machine.

Preferably, the electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the electrical machine includes at least one of a motor anda generator.

There is further provided in accordance with another preferredembodiment of the present invention a method for continuously monitoringat least one machine including sensing magnetic field emission arisingfrom at least one machine and outputting magnetic field emission signalscorresponding to the magnetic field emission, sensing vibrations arisingfrom the at least one machine and outputting vibration signalscorresponding to the vibrations, the sensing of the vibrations beingperformed synchronously with the sensing of the magnetic field emission,receiving at least a portion of the magnetic field emission signals andthe vibration signals and performing analysis of the magnetic fieldemission signals with respect to the vibration signals, and providing anoutput based on the analysis, the output including at least anindication of a condition of the at least one machine and receiving theindication of the condition and initiating at least one of a repairevent on the at least one machine, an adjustment to a maintenanceschedule of the at least one machine and an adjustment to an operatingparameter of the at least one machine based on the indication, wherebyefficacy of the at least one machine is improved.

Preferably, the analysis includes phase analysis of phases of themagnetic field emission signals and the vibration signals.

Preferably, the analysis includes machine-learning functionality.

Preferably, the analysis is performed by at least one data processingmodule and a cloud processing server in communication with the at leastone data processing module.

Preferably, the method also includes continuously sensing at least oneoperational parameter of the at least one machine by a low-powerconsumption sensor having a power uptake of less than or equal to 1microwatt.

Preferably, the low-power consumption sensor is operatively coupled toat least one magnetic sensor and vibration sensor for automaticallycontrolling operation thereof based on the operational parameter.

Preferably, the automatically controlling includes adjusting a samplingperiodicity of at least one of the magnetic and vibration sensor.

Preferably, the at least one machine includes at least one of anelectrical machine and a mechanical machine.

Preferably, the electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the electrical machine includes at least one of a motor anda generator.

There is also provided in accordance with another preferred embodimentof the present invention a method for continuously monitoring at leastone machine including at least near continuously monitoring an operatingparameter of at least one machine by a low-power sensor having a poweruptake of less than or equal to 1 microwatt and outputting signalscorresponding to the operating parameter, receiving at least a portionof the signals and providing an output indication of a condition of theat least one machine based on analysis of the signals and initiatingoperation of at least one additional sensor cooperatively coupled to thelow-power sensor, based on the condition.

Preferably, the low-power sensor includes a vibration sensor and theoperating parameter includes vibrations.

Preferably, the condition includes an on condition or an off condition.

Additionally or alternatively, the condition includes a properlyoperating or improperly operating condition.

Preferably, the improperly operating condition includes one of an actualor impending faulty condition.

Preferably, the additional sensor includes a sensor having a poweruptake greater than the power uptake of the low-power sensor.

Preferably, the additional sensor includes at least one operatingparameter sensor for sensing at least one additional operating parameterof the machine.

Preferably, the additional operating parameter is not the same operatingparameter as the operating parameter sensed by the low-power sensor.

Preferably, the additional sensor includes at least one of a magneticsensor and a vibration sensor.

Preferably, the additional sensor includes at least one magnetic sensorand at least one vibration sensor operating mutually synchronously.

There is also provided in accordance with yet another preferredembodiment of the present invention a method for continuously monitoringat least one machine including at least near continuously monitoring bya low-power sensor an operating parameter of at least one machine andoutputting signals corresponding to the operating parameter receiving atleast a portion of the signals and providing an indication of exceedanceby the signals of a predetermined threshold and initiating operation ofat least one high-power sensor having a power uptake greater than apower uptake of the low-power sensor and cooperatively coupled to thelow-power sensor, based on the indication of exceedance of thepredetermined threshold.

Preferably, the low-power sensor includes a vibration sensor and theoperating parameter includes vibrations.

Preferably, the high-power sensor includes at least one of a magneticsensor and a vibration sensor.

Preferably, the high-power sensor includes at least one magnetic sensorand at least one vibration sensor operating mutually synchronously.

Preferably, the receiving and providing is performed by a CPU coupled tothe low-power sensor.

Preferably, the method also includes connecting the CPU to the at leastone high-power sensor for initiating operation of the at least onehigh-power sensor.

Preferably, the receiving and providing is performed by a cloud-basedsignal analyzer.

Preferably, the predetermined threshold is set based on machinelearning.

Preferably, the low-power sensor has a power uptake of less than orequal to one microwatt.

Preferably, the low-power sensor monitors the operating parameter at asampling rate of at least six times per second.

There is also provided in accordance with a still further preferredembodiment of the present invention a method for continuously monitoringat least one machine including monitoring at least one operationalparameter of at least one machine by at least one sensor with a samplingperiodicity and providing at least one output signal corresponding tothe at least one operational parameter, receiving at least a portion ofthe at least one output signal and performing analysis of the at leastone output signal, and providing an output indication of at least one ofa condition of the at least one machine and a condition of the at leastone sensor based on the analysis and receiving the output indication andadjusting the sampling periodicity in at least near real time basedthereon.

Preferably, the condition of the machine includes an on condition or anoff condition.

Preferably, the condition of the machine includes a properly operatingor improperly operating condition.

Preferably, the condition of the machine includes one of an actual orimpending faulty condition.

Preferably, the condition of the at least one sensor includes a measureof remaining useful life (RUL) of the sensor.

Preferably, the RUL of the sensor includes a measure of remainingbattery life of the sensor.

Preferably, the analysis is performed by a CPU coupled to the sensor.

Preferably, the analysis is performed by a cloud-based signal analyzer.

Preferably, the analysis includes machine-learning functionality.

Preferably, the analysis takes into account a maintenance schedule ofthe at least one machine.

There is also provided in accordance with yet another preferredembodiment of the present invention a method for maintenance of at leastone electrical machine having at least one shared characteristic with aplurality of electrical machines, the method including coupling aplurality of magnetic sensors to a corresponding plurality of electricalmachines having at least one shared characteristic for sensing magneticfields generated thereby, the plurality of magnetic sensors providingoutput indications of the magnetic fields of the corresponding pluralityof electrical machines, receiving the output indications of the magneticfields and providing a correlation output indication of a correlationbetween the magnetic fields and past failures of corresponding ones ofthe plurality of electrical machines, coupling at least one magneticsensor to a given electrical machine having the at least one sharedcharacteristic for providing an individual output indication of magneticfields generated by the given electrical machine, receiving thecorrelation output indication and the individual output indication andproviding a predictive output indication at least of time to failure ofthe given electrical machine, based on applying the correlation outputindication to the individual output indication and providing ahuman-sensible notification including at least the predictive outputindication, at least one of control, repair or maintenance activitiesbeing performed upon the given electrical machine in accordance with thenotification.

Preferably, the given electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, given electrical machine includes an electrical generator.

Preferably, the given electrical machine includes an electrical motor.

Preferably, the given electrical machine includes at least one of an ACor DC electrical machine.

Preferably, the providing the correlation output indication and thepredictive output indication includes machine learning functionality.

Preferably, the shared characteristic includes at least one of a sharedmechanical characteristic, shared electrical characteristic, sharedenvironmental characteristic and shared performance characteristic.

Preferably, the plurality of electrical machines includes the givenelectrical machine.

Alternatively, the plurality of electrical machines does not include thegiven electrical machine.

Preferably, the predictive output indication includes an indication ofan impending fault, the impending fault including at least one of acrawling fault, eccentricity, a damaged rotor bar, a stator short,electrical discharge, mechanical imbalance, energy loss, negative phasesequence and faults arising from extremum operating conditions.

There is also provided in accordance with another preferred embodimentof the present invention a method for automatically alleviatingproblematic conditions in electrical machines due to hacking, the methodincluding associating a plurality of magnetic field sensors with aplurality of electrical machines having at least one sharedcharacteristic, the plurality of magnetic field sensors providinghistorical output indications of magnetic fields generated by theplurality of electrical machines, correlating the magnetic fieldsgenerated by ones of the plurality of electrical machines to at leastone operational parameter in ones of the plurality of electricalmachines and providing a correlation output indication, associating amagnetic field sensor with a given electrical machine having the atleast one shared characteristic for providing an individual outputindication of magnetic fields generated by the given electrical machineand receiving the correlation output indication and the individualoutput indication and providing a hacking responsive control output tothe given electrical machine based on a dissimilarity between thecorrelation output indication and at least one of the individual outputindication and the at least one operational parameter of the givenelectrical machine.

Preferably, the given electrical machine includes at least one of asynchronous and asynchronous electrical machine.

Preferably, the given electrical machine includes an electricalgenerator.

Preferably, the given electrical machine includes an electrical motor.

Preferably, the given electrical machine includes at least one of an ACor DC electrical machine.

Preferably, the correlating includes machine learning functionality.

Preferably, the shared characteristic includes at least one of a sharedmechanical characteristic, shared electrical characteristic, sharedenvironmental characteristic and shared performance characteristic.

Preferably, the plurality of electrical machines includes the givenelectrical machine.

Alternatively, the plurality of electrical machines does not include thegiven electrical machine.

Preferably, the predictive output indication includes an indication ofan impending fault, the impending fault including at least one of acrawling fault, eccentricity, a damaged rotor bar, a stator short,electrical discharge, mechanical imbalance, energy loss, negative phasesequence and faults arising from extremum operating conditions.

There is also provided in accordance with another preferred embodimentof the present invention a method for identifying potential failures andproviding pre-failure alerts for at least one machine having at leastone shared feature with a plurality of machines, the method includingassociating a plurality of operational parameter sensing modules with aplurality of machines having at least one common feature, the pluralityof operational parameter sensing modules providing historical outputindications of at least changes over time in at least one operationalparameter of each of the plurality of machines, correlating patterns ofchanges in the at least one operational parameter in ones of theplurality of machines to past failures in corresponding ones of theplurality of machines and providing a correlation output indication,associating an operational parameter sensing module with a given machinehaving the at least one common feature for providing an individualoutput indication of at least a change over time in the at least oneoperational parameter of the given machine, receiving the correlationoutput indication and the individual output indication and providing apredictive output indication of an impending failure in the givenmachine, based on a similarity between the change over time in the atleast one operational parameter of the given machine indicated by theindividual output indication and the patterns of changes over time inthe at least one operational parameter in the plurality of machinesindicated by the historical output indications, and providing anotification of a status of the given machine based on the predictiveoutput indication, at least one of control, repair or maintenanceactivities being performed upon the given machine in accordance with thenotification.

Preferably, the method also includes playing back an audio signal havingat least one characteristic corresponding to the predictive outputindication of an impending failure.

Preferably, the playing back includes selectively enhancing the at leastone characteristic of the audio signal corresponding to the predictiveoutput indication.

Preferably, the plurality of machines includes at least one ofelectrical machines and mechanical machines.

Preferably, the given machine includes an electrical motor.

Preferably, the given machine includes a generator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

There is also provided in accordance with another preferred embodimentof the present invention a method for optimizing operation of machines,the method including associating a plurality of operational parametersensing modules with a plurality of machines having at least one commonfeature, the plurality of operational parameter sensing modulesproviding historical output indications of at least one operationalparameter of each of the plurality of machines over time, correlatingthe at least one operational parameter in ones of the plurality ofmachines to at least one optimization metric of corresponding ones ofthe plurality of machines and providing a correlator output, associatingan operational parameter sensing module with a given machine having theat least one common feature for providing an individual outputindication of the at least one operational parameter of the givenmachine and receiving the correlator output and the individual outputindication and providing a control output useful for enabling the givenmachine to operate in accordance with an operational parameter which iscorrelated by the correlator output to a desired optimization metric.

Preferably, the plurality of machines includes at least one ofelectrical machines and mechanical machines.

Preferably, the given machine includes an electrical motor.

Alternatively, the given machine includes a generator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

Preferably, the optimization metric includes at least one of machineefficiency, machine power consumption and machine vibration levels.

Preferably, the at least one optimization metric is obtained from anexternal source.

There is also provided in accordance with another preferred embodimentof the present invention a method for automatically alleviatingproblematic conditions in machine systems, the method includingproviding historical output indications by at least one operationalparameter sensing module of at least one operational parameter of atleast one component in a machine system, correlating the historicaloutput indications of the at least one operational parameter tohistorical indications of at least one parameter associated with atleast one other component in the machine system and providing acorrelation output indication, associating an individual operationalparameter sensing module with a given component in a given machinesystem, the given component having at least one feature in common withthe at least one component, for providing an individual outputindication of the at least one operational parameter of the givencomponent and receiving the correlation output indication and theindividual output indication, applying the correlation output indicationto the individual output indication and deriving the at least oneparameter associated with at least one other given component in thegiven system having at least one feature in common with the at least oneother component, and providing a control output to the given systembased on the at least one parameter derived.

Preferably, the component in the machine system is an electrical device.

Additionally or alternatively, the component in the machine system is amechanical device.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

Preferably, the operating parameters of the other given component arenot directly sensed.

Preferably, the given component is cooperatively coupled to the othergiven component in the machine system.

Preferably, the given component is a pump and the other given componentis a chiller.

There is also provided in accordance with another preferred embodimentof the present invention a method for automatically sensing problematicconditions in machine systems due to malicious intervention therewith,the method including associating a plurality of operational parametersensing modules with a plurality of machine systems having at least onecommon feature, the plurality of operational parameter sensing modulesproviding historical output indications of at least one operationalparameter of each of the plurality of machine systems, correlating theat least one operational parameter in ones of the plurality of machinesystems to at least one other parameter in ones of the plurality ofmachine systems and providing a correlation output indication,associating a parameter sensing module with a given machine systemhaving the at least one common feature for providing an individualoutput indication of at least one of the operational parameter and theother parameter of the given machine system and receiving thecorrelation output indication and the individual output indication andproviding an anomaly alert based on a dissimilarity between at least oneof the operational parameter and the other parameter of the givenmachine indicated by the individual output indication and at least oneof the operational parameter and the other parameter indicated by thehistorical output indications.

Preferably, the plurality of machine systems includes at least one ofelectrical machine systems and mechanical machine systems.

Preferably, the given machine system includes an electrical motor.

Additionally or alternatively, the given machine system includes agenerator.

Preferably, the common feature includes at least one of a commonmechanical feature, common electrical feature, common environmentalfeature and common performance feature.

Preferably, the operational parameter includes vibration.

Additionally or alternatively, the operational parameter includesmagnetic fields.

Preferably, the operational parameter includes synchronously sensedmagnetic fields and vibrations.

There is also provided in accordance with another preferred embodimentof the present invention a method for identifying potential failures andproviding pre-failure alerts for at least one machine having at leastone shared feature with a plurality of machines, the method including atleast near continuously sensing, by a plurality of magnetic fieldsensing modules, magnetic fields arising from a plurality of machineshaving at least one common feature, the plurality of magnetic fieldsensing modules providing historical output indications of at leastchanges over time in a magnetic fields of each of the plurality ofmachines, correlating patterns of changes in the magnetic fields in onesof the plurality of machines to past failures in corresponding ones ofthe plurality of machines and to provide a correlation outputindication, associating a plurality of magnetic field sensors with agiven machine having the at least one common feature for synchronouslyproviding a plurality of individual output indications of at least achange over time in the magnetic fields of the given machine, receivingthe correlation output indication and the plurality of individual outputindications and providing a predictive output indication of an impendingfailure in the given machine, based on a similarity between the changeover time in the magnetic fields of the given machine indicated by theplurality of individual output indications and the patterns of changesover time in the magnetic fields in the plurality of machines indicatedby the historical output indications and providing a notification of astatus of the given machine based on the predictive output indication,at least one of control, repair or maintenance activities beingperformed upon the given machine in accordance with the notification.

There is additionally provided in accordance with another preferredembodiment of the present invention a method for identifying potentialfailures and providing pre-failure alerts for at least one machinehaving at least one shared feature with a plurality of machines, themethod including at least near continuously sensing, by a plurality ofvibration sensing modules, vibrations arising from a plurality ofmachines having at least one common feature, the plurality of vibrationsensing modules providing historical output indications of at leastchanges over time in vibrations of each of the plurality of machines, atleast near continuously sensing, by a plurality of magnetic fieldsensing modules, magnetic fields arising from the plurality of machineshaving the at least one common feature, the sensing of the magneticfields being performed synchronously with the sensing of the vibrations,the plurality of magnetic field sensing modules providing historicaloutput indications of at least changes over time in the magnetic fieldsof each of the plurality of machines, correlating patterns of changes inthe vibrations with respect to the magnetic fields in ones of theplurality of machines to past failures in corresponding ones of theplurality of machines and providing a correlation output indication,associating at least one magnetic field sensor with a given machinehaving the at least one common feature for near continuously providingat least one individual output indication of at least a change over timein the magnetic fields of the given machine, associating at least onevibration sensor with the given machine for near continuously providing,synchronously with the near continuously providing by the at least onemagnetic field sensor, at least one individual output indication of atleast a change over time in the vibrations of the given machine,receiving the correlation output indication and the indications ofchanges over time of the magnetic fields and vibrations and providing apredictive output indication of an impending failure in the givenmachine, based on a similarity between the changes over time in themagnetic fields and vibrations of the given machine and the patterns ofchanges over time in the magnetic fields and vibrations in the pluralityof machines indicated by the historical output indications and providinga notification of a status of the given machine based on the predictiveoutput indication, at least one of control, repair or maintenanceactivities being performed upon the given machine in accordance with thenotification.

There is still further provided in accordance with still anotherpreferred embodiment of the present invention a method for optimizingoperation of machines, the method including associating a plurality ofmagnetic sensor modules with a plurality of machines having at least onecommon feature, each of the plurality of magnetic sensor modulessynchronously sensing magnetic fields along at least two signal channelsand providing historical output indications of magnetic fields along theat least two signal channels arising from each of the plurality ofmachines over time, correlating the historical output indications of themagnetic fields in ones of the plurality of machines to at least oneoptimization metric of corresponding ones of the plurality of machinesand providing a correlator output, associating a magnetic sensor modulewith a given machine having the at least one common feature forproviding an individual output indication of magnetic fields arisingfrom the given machine, the magnetic sensor module synchronously sensingmagnetic fields along at least two signal channels and receiving thecorrelator output and the individual output indication, for providing acontrol output useful for enabling the given machine to operate inaccordance with an operational parameter which is correlated by thecorrelator output to a desired optimization metric.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood and appreciated more fullybased on the following detailed description taken in conjunction withthe drawings in which:

FIG. 1 is a simplified schematic illustration of a system for monitoringa machine, constructed and operative in accordance with a preferredembodiment of the present invention;

FIGS. 2A, 2B, 2C, 2D and 2E are simplified respective external andinternal views and a block diagram representation of a sensing moduleuseful in a system of the type illustrated in FIG. 1;

FIGS. 3A, 3B and 3C are simplified respective external and internalperspective views and a block diagram representation of a processing andcommunication module useful in a system of the type illustrated in FIG.1;

FIG. 4 is a simplified schematic illustration of a system for monitoringa plurality of machines, constructed and operative in accordance withanother preferred embodiment of the present invention;

FIGS. 5A and 5B are simplified flow charts illustrating signalprocessing functionality of systems of the types shown in FIGS. 1-4;

FIG. 6 is a simplified graphical representation of magnetic field datasynchronously acquired along multiple channels by a system of any of thetypes illustrated in FIGS. 1-4, as measured for a properly operatingasynchronous electrical machine;

FIGS. 7A, 7B and 7C are respective orbit plots based on data acquired bysynchronous magnetic sampling along multiple channels for a properly andimproperly operating asynchronous electrical machine, as acquired by asystem of any of the types illustrated in FIGS. 1-4;

FIG. 8 is a simplified graphical representation of magnetic field dataacquired along a single channel and vibrational data synchronouslyacquired along multiple channels by a system of any of the typesillustrated in FIGS. 1-4, as measured for a properly operatingasynchronous electrical machine;

FIG. 9 is a phase plot based on data acquired by magnetic sampling alonga single channel and synchronous vibrational sampling along multiplechannels for a properly and improperly operating asynchronous electricalmachine, as acquired by a system of any of the types illustrated inFIGS. 1-4;

FIG. 10 is a simplified graph displaying magnetic and vibrational datasynchronously acquired along multiple channels by a system of any of thetypes illustrated in FIGS. 1-4, as measured for a properly operatingasynchronous electrical machine;

FIG. 11 is a phase plot based on data acquired by synchronous magneticand vibrational sampling along multiple channels for a properly andimproperly operating asynchronous electrical machine, as acquired by asystem of any of the types illustrated in FIGS. 1-4;

FIG. 12 is an orbit plot representation of data acquired by synchronousmagnetic and vibrational sampling along multiple channels for animproperly operating mechanical device coupled to an asynchronouselectrical machine, as acquired by a system of any of the typesillustrated in FIGS. 1-4;

FIG. 13 is a simplified schematic illustration of a system formonitoring a machine, constructed and operative in accordance withanother preferred embodiment of the present invention;

FIG. 14 is a simplified graphical representation of magnetic andvibrational data synchronously acquired by a system of the typeillustrated in FIG. 13, as measured for a properly operating synchronouselectrical machine;

FIGS. 15A and 15B are simplified graphs displaying magnetic field datasynchronously acquired along multiple channels by a system of the typeillustrated in FIG. 13, as respectively measured for a properlyoperating and improperly operating synchronous electrical machine;

FIGS. 16A and 16B are simplified graphs respectively displaying magneticfield data and vibration data synchronously acquired along multiplechannels by a system of the type illustrated in FIG. 13, for animproperly operating synchronous electrical machine;

FIG. 17 is a phase plot based on data acquired by synchronous magneticand vibrational sampling along multiple channels for an improperlyoperating synchronous electrical machine, as acquired by a system of thetype illustrated in FIG. 13;

FIG. 18 is a simplified graphical representation of data showing trendsin energy consumption for an electrical machine, as acquired by a systemof any of the types illustrated in FIGS. 1, 4 and 13;

FIG. 19 is a simplified graph displaying data showing trends inefficiency for an electrical machine, as acquired by a system of any ofthe types illustrated in FIGS. 1, 4 and 13;

FIG. 20 is a simplified schematic illustration of a system formonitoring multiple machines, constructed and operative in accordancewith yet another preferred embodiment of the present invention;

FIG. 21 is a simplified schematic partially pictorial, partially blockdiagram illustration of a portion of the system illustrated in FIG. 20,constructed and operative in accordance with a preferred embodiment ofthe present invention;

FIG. 22 is a simplified schematic partially pictorial, partially blockdiagram illustration of a portion of the system illustrated in FIG. 20,constructed and operative in accordance with another preferredembodiment of the present invention;

FIG. 23 is a simplified visualization of a concept of event drivenmachine diagnosis;

FIG. 24 is a graphical representation of patterns of change inoperational parameters of a machine during development of a fault in themachine;

FIG. 25 is a simplified visualization of event driven machine diagnosiscorresponding to the patterns of change displayed in FIG. 24;

FIG. 26 is a simplified illustration of a portion of a system forautomated monitoring and control of a machine, constructed and operativein accordance with yet a further preferred embodiment of the presentinvention; and

FIG. 27 is a simplified illustration of a system for monitoring multiplemachines within a single system, constructed and operative in accordancewith a still further preferred embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Reference is now made to FIG. 1, which is a simplified schematicillustration of a system for monitoring a machine, constructed andoperative in accordance with a preferred embodiment of the presentinvention.

As seen in FIG. 1, there is provided a system 100 for monitoring,preferably although not necessarily continuously, operation of at leastone machine 102. Machine 102 may be one or more of a mechanical machineand/or an electrical machine, which electrical machine may be anasynchronous electrical machine, such as an asynchronous motor orgenerator, or a synchronous electrical machine such as a synchronousmotor or generator. Machine 102 may furthermore be embodied as analternating current (AC) or direct current (DC) electrical machine.Here, by way of example, at least one machine 102 is seen to be embodiedas an asynchronous electrical motor 104 cooperatively coupled to amechanical device 106, here embodied by way of example as a pump 106.Mechanical device 106 may be any type of mechanical device suitable forcooperation with asynchronous motor 104, including a fan, chiller,compressor or turbine.

Operation of motor 104 and mechanical device 106 is preferably monitoredby at least one sensor module 110 for sensing operating parameters of atleast one of motor 104 and device 106. Here, by way of example, at leastone sensor module 110 is seen to be embodied as a first sensor module112 and a second sensor module 116 mounted on motor 104 and anadditional third sensor module 118 mounted on device 106. It isappreciated, however, that system 100 may include a fewer or greaternumber of sensor modules 110 distributed between motor 104 and device106.

Sensor modules 112-118 are preferably mounted on various locations ofthe frames of motor 104 and device 106, for example in close proximityto machine bearings. Multiple ones of sensor modules associated with aparticular machine, such as sensor modules 112 and 116 associated withmotor 104, may be mounted at mutually similar orientations or may bemounted at different orientations depending on the nature of theoperating parameter to be sensed and monitored thereby. Sensor modules110 may be in physical contact with the machine monitored thereby, ashere illustrated to be the case for sensor modules 112-118 with respectto motor 104 and device 106. Alternatively, sensor modules 110 may bephysically offset from the machine monitored thereby, provided thatsensor modules 110 are positioned so as to be capable of sensing atleast one operating parameter of the machine to be monitored thereby.

Each sensor module 110 preferably comprises at least one sensor, andmore preferably a collection of sensors, for sensing at least oneoperating parameter of at least one of motor 104 and device 106. As seenmost clearly at an enlargement 120, sensor module 110 may comprise atleast one magnetic sensor 130 for sensing magnetic fields emitted bymotor 104. Sensor module 110 may additionally comprise at least onevibration sensor 140 for sensing vibrations arising from motor 104,device 106 or both. Sensor module 110 may further include additionalsensors for sensing a variety of operational parameters including, butnot limited to, temperature and acoustic emission, as is detailedhenceforth.

It is a particular feature of a preferred embodiment of the presentinvention that in the case that motor 104 is monitored by at least twomagnetic sensors 130, the at least two magnetic sensors 130 arepreferably operative to mutually synchronously sense magnetic fieldsemitted by the at least one machine being monitored thereby, hereembodied as motor 104, along a corresponding plurality of signalchannels. The two or more magnetic sensors 130 may be included in asingle sensor module 110 or in multiple individual ones of sensor module110. It is understood that in the context of the present invention,sensing by two or more sensors may be considered to be synchronous whenthe sampling difference in time between the sensors is of the order ofless than or equal to about 0.01/F_(s) where F_(s) is the sensorsampling frequency.

It is a further particular feature of a preferred embodiment of thepresent invention that in the case that motor 104 is monitored by atleast one magnetic sensor 130 and at least one vibration sensor 140, theat least one magnetic sensor 130 and at least one vibration sensor 140are operative to synchronously sense magnetic fields and vibrationsemitted by the at least one machine being monitored thereby, hereembodied as at least one of motor 104 and device 106. The at least onemagnetic sensor 130 and at least one vibration sensor 140 may both beincluded in a single sensor module 110 or in separate ones of sensormodule 110.

Further details pertaining to the preferable structure and operation ofsensor module 110 are provided henceforth with reference to FIGS. 2A-2E.

Sensor module 110 preferably includes communication functionality and ispreferably adapted for wireless communication with at least one dataprocessing module 150. Data processing module 150 is preferablyoperative to receive signals, preferably wirelessly, from ones of sensormodule 110. Furthermore, data processing module 150 is preferablyoperative to control sensor module 110 and particularly preferably tosynchronize between ones of sensor module 110, as required in order forsensor modules 110 to perform synchronous magnetic and/or magnetic andvibrational sensing as described hereinabove.

Each data sample from sensor module 110 is preferably collected for apredetermined duration of time at a predetermined sampling frequency,for example, for a duration of 4 seconds at a 20 kHz sampling frequency.Samples are collected periodically, for example, once an hour. Theperiodicity with which data samples are collected may be referred to asthe sampling periodicity or number of data acquisition cycles.

Preferably, a single data processing module 150 is operative to receivedata in the form of signals from multiple ones of sensor module 110mounted on at least one machine, here embodied, by way of example asthree sensor modules 112, 116 and 118 mounted on motor 104 and device106 and all in communication with data processing module 150.

Data processing module 150 may be located remotely from the varioussensor modules 110 in communication therewith, provided that dataprocessing module 110 is capable of receiving signals from the varioussensor modules 110. By way of example, data processing module 150 may bemounted on the wall of a room in which motor 104 and device 106 arelocated.

At least a portion of the data received at data processing module 150 ispreferably transmitted thereby to a server 160, typically on the cloud,for processing. In addition to transmitting data to server 160, dataprocessing module 150 may also be operative to itself perform edgeanalysis of the data and to control sensor modules 110 based on theresults of the edge analysis performed thereby. Such edge analysis mayserve to reduce the quantity of data required to be transmitted toserver 160.

Further details pertaining to the preferable structure and operation ofdata processing module 150 are provided henceforth with reference toFIGS. 3A-3C.

Server 160 is preferably operative to receive data from at least onedata processing module 150 and to analyze the data in accordance withautomatic algorithms, preferably including machine learning algorithms.Analysis of data by server 160 may include processing of information ina cloud server as described in U.S. Pat. No. 9,835,594, filed Oct. 22,2012 and entitled AUTOMATIC MECHANICAL SYSTEM DIAGNOSIS, the disclosureof which is hereby incorporated by reference.

Analysis of data by server 160 may include the execution of algorithmsfor detection of a condition of motor 104 and/or device 106, includingdetection or prediction of mechanical and electrical faults, efficiencyanalysis and analysis of degradation of performance of motor 104 and/ordevice 106. Furthermore, analysis of data by server 160 may be used toidentify possible security breaches in control of motor 104 and/ordevice 106, due for example to hacking or other malicious activitiesdirected against motor 104 and/or device 106 via computerized controlsthereof.

Further details pertaining to the processing steps performed by server160 are provided henceforth below with reference to FIGS. 5A and 5B and20-26.

At least one of data processing module 150 and server 160 preferablyprovides an output 170 based on the analysis performed thereby, whichoutput 170 preferably includes at least an indication of a condition ofthe at least one machine being monitored, such as motor 104 and/ordevice 106.

It is appreciated that data processing module 150 in combination withserver 160 thus preferably constitutes a particularly preferredembodiment of a signal analyzer 172, receiving at least a portion of thesignals sensed by sensor module 110, performing analysis of the signalsand providing an output based on the analysis, which output preferablyincludes at least an indication of a condition of the at least onemachine being monitored.

Classification of the continuous data measured by ones of sensor module110 as received by data processing module 150 and server 160 may bebased on the modeling of valid system performance and comparison ofcurrent system performance with known performance of the same systemunder similar operating conditions in the past.

Any type of suitable model may be used for data classification,including a statistical model or machine learning model. Additionaltypes of models may include nearest neighbor or any other probabilitydensity function estimation and classification methods such as Parzenwindows or SVM. Models may be created from characteristic signatures orbased on features provided by other data processing algorithms. Forexample, the combined normalized energy of detected harmonic seriesrelative to the total signal energy may be generated by an algorithm fordetection of non-synchronous harmonic series.

The machine model may be updated during a learning period, in order tocollect data corresponding to as many machine operating conditions aspossible during workload changes.

Several models may be associated with the same machine. A machine mayhave a simple model associated therewith, which simple model may beprocessed on low computing power devices included in sensor module 110.Additionally or alternatively, a machine may have a more complex modelassociated therewith, which more complex model may be processed bymedium power computing devices included in data processing module 150.Still additionally or alternatively, a machine may have a highly complexmodel associated therewith, which highly complex model may be processedby high power computing devices such as at cloud server 160. Models ofdifferent complexity facilitate optimization of overall performance ofsystem 100 and may significantly improve efficiency.

Data collected by sensor module 110 is preferably compared to thelow-complexity model. If the data does not fit the model, the data maybe sent to data processing module 150, at which the data is compared tothe medium complexity model. If the data does not fit the mediumcomplexity model, the data may be sent to cloud server 160 and comparedto the high complexity model. In cases that a significant deviation isfound between the data and the high complexity model, the model may beupdated. Alternatively, an alert such as an alert 174 may be issuedregarding machine performance.

Algorithms employed in data processing module 150 and server 160 may beused to build a baseline for motor 104 and/or device 106 being monitoredand to detect deviations and anomalies, for example in magnetic fielddata, acquired for the monitored machine with respect to the baseline.Particularly preferably, the algorithms used both to build a baselineand to detect deviation therefrom are machine learning algorithms,operating in the time and/or frequency domain.

The baseline signal, such as a baseline magnetic signal, andcorresponding deviation therefrom may be one or more of anexperimentally determined threshold signal associated with a givenmachine, exceedance of which is indicative of an incipient or actualmachine fault; a historical magnetic signal or set of signals associatedwith a particular machine, deviation from which by a given statisticalmeasure is indicative of an incipient or actual machine fault and acollection of historical magnetic signals or set of signals fromcorresponding although not necessarily identical machines, deviationfrom which by a given statistical measure is indicative of an actual orincipient fault in the machine. Alternatively machine learningtechniques such as anomaly detection, for example, may be employed todetect deterioration from a known machine condition. Such faults mayinclude, by way of example only, crawling faults, eccentricity, damagedrotor bars, electrical shorts such as winding shorts, electricaldischarge, load instabilities, power source problems such as in VFDsystems, unbalanced magnetic polls and mechanical imbalance of motor 104and device 106, as is described in further detail henceforth.

Machine learning algorithms may also be useful in identifying whetherelectrical machines such as motor 104 have been affected by hacking orother malicious activities directed against the electrical machine viacomputerized controls thereof. In the case that an electrical machine orcontroller thereof is subject to a malicious attack, the incomingcurrent and hence magnetic field emission may deviate with respect tobaseline magnetic emission patterns established during regular,non-interfered operation of that particular machine or other similarmachines.

Here, by way of example only, sensor modules 112, 116 and 118 preferablysimultaneously sense at least magnetic fields emitted by motor 104during operation thereof. Each of sensor modules 112, 116, 118preferably includes a single magnetic sensor 130, such that magneticfields emitted by motor 104 are sensed by a total of three magneticsensors 130, which three magnetic sensors 130 preferably operatesynchronously with respect to each other as regulated by data processingmodule 150. Each magnetic field sensor 130 preferably outputs a magneticfield emission signal corresponding to the magnetic field sensed therebyalong a corresponding signal channel, which signal is preferablyreceived by data processing module 150 and transmitted thereby to cloudserver 160. It is appreciated that server 160 thus preferably receivesmagnetic field emission data sensed simultaneously along three signalchannels in the embodiment of monitoring system 100 illustrated in FIG.1.

Server 160, and optionally data processing module 150, preferablyanalyzes the magnetic field emission signals received thereat andprovides an output based on the analysis. Here, the analysis performedat server 160, and optionally at data processing module 150, preferablyincludes phase analysis of the simultaneously sampled magnetic signalssensed by sensor modules 112, 116 and 118, as indicated by a phaseanalysis graph 176. Such phase analysis may be useful in deriving acondition of motor 104 and/or device 106. It is appreciated, however,that the analysis performed at one or both of data processing module 150and server 160 does not necessarily include phase analysis, nor islimited to phase analysis, and may involve any type of data processingand analysis as is known in the art in order to provide an outputindication of a condition of the machine being monitored.

System 100 further preferably includes a control module 180, receivingthe indication of a condition of the machine being monitored from server160 and/or data processing module 150. Control module 180 may be anycomputing device, such as a computer 182 or personal communicationdevice 184 illustrated herein. Control module 180 preferably initiatesat least one of a repair event on the at least one machine beingmonitored, an adjustment to a maintenance schedule of the at least onemachine and an adjustment to an operating parameter of the at least onemachine based on the indication provided thereto.

Here, by way of example, magnetic signal phase analysis performed byserver 160 and/or data processing unit 150 may automatically yield anindication of an actual or incipient fault in motor 104. Control module180 may receive indication of the fault predicted or detected andrepair, deactivate or otherwise adjust motor 104 responsively. It isappreciated that the control actions performed by control module 180thus preferably serve to improve the efficacy of the at least onemachine being monitored.

Reference is now made to FIGS. 2A-2E, which are simplified respectiveexternal, internal first and second perspective and side views and ablock diagram representation of a sensor module useful in a system ofthe type illustrated in FIG. 1.

As seen most clearly in FIG. 2A, sensor module 110 preferably comprisesan external cover 202 housing the internal components thereof. Aprotrusion 204 is preferably formed at a base 206 of external cover 202,which protrusion 204 is adapted for mounting sensor module 110 on themachine 102 to be monitored thereby. By way of example, protrusion 204may be attached to a stud (not shown), which stud may be screwed, gluedor otherwise mounted on machine 102. Cover 202 preferably includes anupper portion 210 preferably formed of a rigid material such as a metaland a lower portion 212 preferably formed of a rigid dielectric materialsuch as plastic, so as not to attenuate the magnetic signal arising frommotor 104 as sensed by magnetic sensor 130 within sensing module 110.

Turning now to FIGS. 2B-2E, sensing module 110 is seen to preferablycomprise a plurality of sensors for sensing operating parameters ofmachine 102, preferably including at least one magnetic field sensor130. Magnetic field sensor 130 may be embodied as any type of magneticsensor including, for example, an analog Hall bar magnetic sensor. Inone embodiment of the present invention, magnetic field sensor 130 mayoperate at a bandwidth of approximately 40 kHz, a sampling frequency ofgreater than or equal to approximately 20 kH, may draw a current ofseveral mA and may operate at a power of approximately 1 milliWatt ormore. However, it is appreciated that these values are exemplary onlyand may be readily varied in accordance with the desired operatingspecifications of magnetic field sensor 130.

Magnetic sensor 130 is preferably operative to provide continuouswide-band high resolution measurement of synchronized magnetic fielddata to be processed by algorithms included in data processing unit 150and/or server 160. Such magnetic field data may be useful in analyzing acondition of the machine 102 being monitored by sensor module 110,including, by way of example only, motor speed detection, detection ofanomalies in the operation of the machine 102 being monitored, detectionof mechanical and electrical faults, and electrical energy loss andefficiency analysis.

Additionally, the collection of magnetic field data by magnetic sensor130 in synchrony with other ones of magnetic sensor 130 and/or insynchrony with sensing of vibrations by vibration sensor 140,facilitates the performance of phase analysis on the magnetic signals,which phase analysis may serve to enhance the data analysis.

It is appreciated that although sensor module 110 is shown here toinclude only a single magnetic sensor 130, sensor module 110 may includea greater number of magnetic fields sensors located along one or moreaxes. Sensor module 110 and magnetic sensor 130 therein may beorientated to sense axial or radial magnetic fields, in accordance withthe desired analysis to be performed thereupon. For example, magneticsensor 130 may be embodied as a tri-axial magnetic sensor synchronouslysampling magnetic fields along three axes.

Sensor module 110 preferably additionally includes at least onevibration sensor 140, here embodied, by way of example, as a firstanalog accelerometer 220, a second analog accelerometer 222 and a thirdanalog accelerometer 224. First, second and third accelerometers 220,222, 224 are preferably tri-axially positioned, for respectively sensingvibrations along three mutually perpendicular axes of machine 102.Accelerometers 220, 222, 224 preferably operate mutually simultaneouslyand preferably, although not necessarily, synchronously with at leastone magnetic sensor 130.

Accelerometers 220, 222, 224 are preferably operative to providecontinuous wide-band high resolution measurement of preferablysynchronized vibration data to be processed by algorithms included indata processing unit 150 and/or server 160. Accelerometers 220, 222, 224may operate at any sampling frequency and appropriately calibratedbandwidth. By way of example only, accelerometers 220, 222, 224 mayoperate at a bandwidth of approximately 11 kHz, a sampling frequency ofgreater than or equal to approximately 20 kHz and draw a current ofseveral mA.

Vibration data sensed by accelerometers 220, 222, 224 may be useful indetection of anomalies in machine operation, in detection of actual orincipient faults, in analysis of mechanical energy loss and efficiencyand in phase analysis both of multiple synchronous vibration signals andmultiple synchronous vibration and magnetic field signals.

Sensor module 110 preferably additionally includes at least onelow-power consumption sensor 230. Low power sensor 230 may be embodiedas any suitable type of digital or analogue low-power sensor, includingbut not limited to a low-power acoustic sensor, low-power vibrationsensor, low-power magnetic sensor and low-power temperature sensor.Low-power sensor 230 preferably has a power uptake significantly lowerthan that of other operational parameter sensors, such as magneticsensor 140 and vibration sensor 140, included in sensor module 110. Byway of example, low-power sensor 230 may draw a current of less than onemicroampere and may consume a power of approximately 1 microwatt orless.

Low-power sensor 230 preferably monitors an operating parameter ofmachine 102, such as vibration, at a relatively high sampling rate, suchas approximately six times per second and preferably outputs signalscorresponding to the operating parameter monitored thereby. Low-powersensor 230 thus preferably provides at least near real time, at leastnear continuous high resolution measurements of a given operatingparameter, preferably at a low bandwidth of less than 1 kHz.

Due to the low power consumption by low-power sensor 230, the continuoussampling performed by low-power sensor 230 preferably has a minor ornegligible effect on the overall power consumption by sensor module 110.Low-power sensor 230 is preferably operatively coupled to at least oneadditional sensor, such as at least one of magnetic sensor 130 andvibration sensor 140, for automatically controlling operation thereofbased on the operational parameter sensed thereby. The at least oneadditional sensor may sense the same or a different operationalparameter than the operational parameter sensed by low-power sensor 230.

Low-power sensor 230 is preferably connected to a sampling circuit 232,which sampling circuit 232 is preferably connected to a signal analyzer,here embodied, by way of example, as a CPU 234. During operation oflow-power sensor 230, low-power sensor 230 may continuously sample agiven operating parameter of machine 102, such as vibrations, and outputsignals corresponding to the measured operating parameter.

CPU 234 is preferably operative to receive at least a portion of thesignals output by low-power sensor 230 and to perform an analysis of thesignals in order to ascertain a condition of the machine 102 beingmonitored thereby.

Such analysis may include detection of possible exceedance by thesignals of at least one predetermined threshold, which at least onepredetermined threshold may be set based on machine-learning methods.For example, low-power sensor 230 may be embodied as a low-powervibration sensor continuously monitoring vibrations of motor 104 and/ordevice 106 and providing vibration data to CPU 234. In this case theanalysis performed by CPU 234 may include detecting deviations in thevibration energy, possibly based on machine learning methods. Upon CPU234 finding deviations in the measured vibration energy to exceed apredetermined vibration energy deviation threshold, operation ofadditional sensors included in sensor module 110, such as higher poweredmagnetic sensor 130 and vibration sensor 140, may be initiated.

Further by way of example, detection of possible exceedance of at leastone predetermined threshold by the signals provided by low-power sensor230 may be performed in order to ascertain whether motor 104 or device106 is in an ‘on’ or ‘off’ state. In the case that the analysisperformed by CPU 234 finds machine 102 to be in an ‘on’ state, based onexceedance by the measured signals of at least one predeterminedthreshold, operation of additional sensors included in sensor module110, such as higher powered magnetic sensor 130 and vibration sensor140, may be initiated. By way of example, operation of magnetic sensor130 and vibration sensor 140 may be initiated by way of CPU 234 wakingup a data acquisition unit such as an ADC 236 cooperatively coupled tomagnetic sensor 130 and vibration sensor 140.

By way of example, in the case that low-power sensor 230 is embodied asa low-power vibration sensor, power consumption by low-power sensor 230operating in an on-off detection mode may be approximately 0.54microwatts with a current uptake of approximately 0.27 microamps and avoltage uptake of approximately 2 V.

Additionally or alternatively, the analysis of the signals fromlow-power sensor 230 by CPU 234 may include derivation of a condition ofthe machine being monitored thereby, such as motor 104 and device 106.For example, CPU 234 may include algorithms for detecting anomalies inoperation of the machine being monitored, for detecting actual orimpending faults in the machine being monitored or for evaluatingmechanical energy loss and resultant efficiency.

Operation of additional sensors included in sensor module 110, such ashigher powered magnetic sensor 130 and vibration sensor 140, may beadjusted based on the condition of the machine ascertained by CPU 234.By way of example, operation of magnetic sensor 130 and vibration sensor140 may be initiated by way of CPU 234 waking up ADC 236 cooperativelycoupled to magnetic sensor 130 and vibration sensor 140. Alternatively,a sampling periodicity or frequency of operation of magnetic sensor 130and vibration sensor 140 may be adjusted based on the detectedcondition.

By way of example, in the case that low-power sensor 230 is embodied asa low-power vibration sensor, power consumption by low-power sensor 230operating in a condition detection mode may be of the order ofapproximately 6 microwatts, with a current uptake of approximately 3microamps and a voltage uptake of approximately 2 V.

The output of the analysis performed by CPU 234, including detection ofthe on or off state of the machine or other machine condition such asproper or improper operation, is preferably sent to data processingmodule 150, which data processing module 150 preferably transmits atleast a part of the data to cloud server 160.

It is appreciated that the inclusion of a low-power sensor such aslow-power sensor 230 in sensor module 110 is optional only. Furthermore,the function of low-power sensor 230 may be replaced by higher powersensors, such as at least one of magnetic sensor 130 and vibrationsensor 140, which higher power sensors may be additionally operative ina low-power mode. In a low-power mode, at least one of magnetic sensor130 and vibration sensor 140 if present may continuously sense, forexample multiple times per second, at least one operating parameter ofthe machine being monitored and provide corresponding signals to asignal analyzer included in sensor module 110, such as CPU 234.

In the case that analysis of the signals by CPU 234 finds the machinebeing monitored to be in a faulty or impending faulty condition, CPU 234may adjust the operating characteristics of magnetic sensor 130 and/orvibration sensor 140. Additionally or alternatively, CPU 234 may providean output indicative of the actual or incipient fault to cloud server160 by way of data processing module 150. Cloud server 160 may providean output to control module 180 including an indication of the fault andprompting the initiation of additional higher power monitoring. Suchhigher power monitoring may be provided, by way of example, in the formof use of an external sampling unit by a user or by the initiation ofhigher power operation of at least one of magnetic sensor 130 andvibration sensor 140.

In one possible embodiment of the present invention, at least one sensorsuch as magnetic sensor 130 and vibration sensor 140 may monitor atleast one operational parameter of at least one machine with a samplingperiodicity and provide at least one output signal corresponding to theat least one operational parameter monitored thereby. A signal analyzer,such as CPU 234, data processing module 150 or cloud server 160 mayreceive at least a portion of the at least one output signal and performanalysis of the at least one output signal. The signal analyzer mayprovide an output indication of at least one of a condition of the atleast one machine and a condition of the at least one sensor based onthe analysis. A control module may receive the output indication andadjust the sampling periodicity of the at least one sensor in at leastnear real time based on the output indication. For example, the samplingperiodicity of sensors in sensor module 110 may be adjusted based onwhether the machine is in an on or off condition, a properly orimproperly operating condition, and an actual or impending faultycondition. Such conditions may be ascertained based on machine learningfunctionality or by other methods.

In one possible embodiment, system 100 may perform an estimation of theremaining useful life (RUL) of motor 104 or device 106 based on thesignals sensed by sensor module 110. Such an RUL estimation may beperformed at CPU 234, at data processing module 150 or at cloud server160. Based on the RUL, the sampling periodicity of the higher powersensors, including magnetic sensor 130 and/or vibration sensor 140, maybe adjusted. By way of example, for machines with a longer RUL thesampling periodicity of sensors in sensor module 110 may be reduced,whereas for machines with a shorter RUL, the sampling periodicity ofsensors in sensor module 110 may be increased, in order to accuratelydetect early signs of machine deterioration.

It is appreciated that the description of the inclusion in sensor module110 of at least one magnetic sensor, such as magnetic sensor 130, atleast one vibration sensor, such as tri-axial vibration sensors 220,222, 224 and a low-power sensor, such as low-power sensor 230, is by noway intended to be limiting and sensor module 110 may include additionalsensors 238 and corresponding sampling components 239, such as, by wayof example only, temperature, humidity, optical and acoustic sensors.

ADC 236 is preferably connected to the sensors from which data isacquired thereby by way of at least one amplifier 240 and at least onefilter 242, for enhancing the signal quality. CPU 234 is preferablyoperative to control the sensors included in sensor module 110, such amagnetic sensor 130 and vibration sensors 220, 222, 224 and low-powersensor 230, and perform analysis on data collected thereby, as detailedhereinabove. Sensor module 110 preferably includes a memory 250 forstorage of data therein. Sensor module 110 additionally preferablyincludes a communication component 260, such an antenna, forcommunicating with data processing module 150.

Sensor module 110 is preferably powered by a battery 262. Battery 262 ispreferably enclosed by a cage 264, which cage 264 preferably holdsbattery 262 extremely tightly so as to prevent vibration thereof duringmachine operation. It is appreciated that should battery 262 not be heldsufficiently tightly within cage 264 vibrations of battery 262 may besensed by vibration sensors 220, 222, 224 and thus potentially distortthe vibration data sensed thereby.

It is appreciated that battery 262 may alternatively be held in anexternal unit connected to sensor module 110, rather than within thebody of sensor module 110. This may be advantageous in that such anarrangement renders sensor module 110 more compact, allowing sensormodule 110 to be easily mounted even within small available spaces onthe machine to be monitored thereby.

In one preferred embodiment of the present invention, operation ofsensor module 110 may be optimized based on remaining battery life ofbattery 262. By way of example, the sampling periodicity of sensors insensor module 110 may be reduced in the case of low remaining batterylife of battery 262, in order to maintain system 100 as operational foras long as possible before battery 262 requires replacement. Thereduction in sampling periodicity may be based on an expected batteryreplacement schedule. For example, if a fully charged battery is capableof 10,000 cycles of data acquisition, a battery 262 with 5% remaininguseful life is capable of 500 cycles of data acquisition. If batteryreplacement is scheduled to be carried out in 50 days, system 100 may belimited to perform no more than ten data acquisition cycles per day, inorder for battery 262 to sustain system 100 until such time as battery262 is replaced. Algorithms for detecting remaining battery life andautomatically changing the sampling regime may be included in one ormore of CPU 234, data processing module 150 and cloud server 160.

Reference is now made to FIGS. 3A, 3B and 3C, which are simplifiedrespective external and internal perspective views and a block diagramrepresentation of a processing and communication module useful in asystem of the type illustrated in FIG. 1.

As seen in FIGS. 3A-3C, data processing module 150 preferably includes afirst communication component 302 for receiving incoming signals from atleast one sensor module 110 and a second communication component 304 fortransmitting signals to cloud server 160. Preferably, first and secondcommunication components 302, 304 are antennas operative to wirelesslyrespectively receive and transmit signals.

Data processing module 150 further preferably includes a CPU 306, amemory 308 and data storage disk 310. CPU 306 is preferably operative toperform local analysis of data received from sensor modules 110. Suchlocal analysis may include anomaly detection, fault detection andderivation of a machine condition. In some embodiments, CPU 306 may becapable of running all or part of the analysis algorithms held in cloudserver 160. Data processing module 150 may also include at least oneadditional sensor 320. For example, additional sensor 320 may beembodied as a temperature sensor for sensing environmental temperatureconditions, which may be used as a basis for deriving the temperature ofthe machine being monitored.

Data processing module 150 is preferably operative to synchronizeoperation of the various sensor modules 110 in communication therewith.Here, for example, data processing module 150 is preferably operative tosynchronize operation of three sensor modules 112, 116 and 118 incommunication therewith. Sensor modules 112, 116 and 118 preferablyoperate synchronously under the control of a single data processingmodule 150 to provide overall twelve synchronized sensors, including atotal of three sets of three tri-axial accelerometers 220, 222, 224 anda total of three magnetic sensors 130 included in sensor modules 112,116 and 118.

In the case that the twelve synchronized sensors, including threemagnetic sensors 130 and nine accelerometers 220,222, 224, eachoperating at a sampling frequency of approximately 20 kHz, the samplingof each of the twelve sensors is preferably synchronized by dataprocessing module 150 to be performed at least near simultaneouslyacross all of the sensors. It is understood that in the context of thepresent invention, sensing by two or more sensors may be considered tobe synchronous when the sampling difference in time between the sensorsis of the order of less than or equal to about 0.01/F_(s) where F_(s) isthe sensor sampling frequency. By way of example, the sampling performedacross all of the sensors may be synchronized to within 1 microsecond orwithin several microseconds.

It is appreciated that the description of twelve synchronized sensorsproviding synchronized magnetic and vibration signals along twelvechannels is exemplary only, and that system 100 may be scaled to providean even greater number of synchronized signals along a correspondingnumber of channels, depending on the number of sensor modules 110employed.

It is understood that although data processing module 150 is illustratedherein as being in communication with a single motor 104 and device 106,system 100 may be adapted to include a plurality of electrical and/ormechanical machines 102 in communication with one or more dataprocessing modules 150, as shown to be the case for a monitoring system400 in FIG. 4.

As seen in FIG. 4, monitoring system 400 preferably includes a pluralityof electrical and/or mechanical machines 402 here embodied, by way ofexample, as a large number ‘n’ of asynchronous motors 104 each coupledto device 106 and monitored by a corresponding plurality of sensormodules 110, as described hereinabove with reference to system 100 ofFIG. 1. It is appreciated, however, that the illustration of pluralityof electrical and/or mechanical machines 402 as comprising a pluralityof asynchronous motors 104 and machines 106 is exemplary only and thatplurality of electrical and/or mechanical machines 402 may comprise anytype of electrical and/or mechanical machines including synchronous orasynchronous, AC or DC electrical machines.

Plurality of sensor modules 110 may be in communication with a singledata processing module 150 or with more than one data processing module150, depending on the number and spatial distribution of plurality ofmachines 402. The at least one data processing module 150 included insystem 400 is preferably in communication with cloud server 160. Cloudserver 160 preferably includes algorithms to analyze signals sensed bysensor modules 110 and communicated to cloud server 160 via at least onedata processing module 150. Feedback control is preferably provided toat least one of plurality of machines 402 based on the results of theanalysis performed by automatic algorithms included in at least one ofdata processing module 150 and cloud server 160.

It is understood that although plurality of machines 402 is here shownto comprise a plurality of n identical and co-located machines, this isnot necessarily the case. Rather, system 400 may monitor a plurality ofnon-identical machines co-located or remotely located with respect toeach other, which plurality of machines preferably share at least onecommon mechanical or electrical characteristic. By way of example, theplurality of machines being monitored by system 400 may have a commonmechanical structure; common motor type such as part number; share acommon environmental characteristic such as co-located ones of motor104; share a common operating parameter such as load, temperature orhumidity; or share a common operational purpose or performancecharacteristic, such as motors working in parallel on the same task orsimilar tasks.

It is appreciated that the employment of system 400 to monitor a largenumber of similar although not necessarily identical machines sharing atleast one common mechanical or electrical characteristic, allows system400 to operate in accordance with a crowd-sourcing approach. In such acrowd-sourcing implementation of system 400, server 160 preferablyaccumulates data from a large population of similar or identicalmechanical and/or electrical machines, at least one operating parameterof each of which machines is preferably monitored by at least one sensormodule 110 in communication with a data processing module 150.

The accumulation of operational parameter data for a large population ofelectrical or mechanical machines sharing at least one electrical ormechanical characteristic based on crowd sourcing is highlyadvantageous, due to the enhancement of the reliability and robustnessof the analysis performed at server 160 and the accuracy of thecondition classification of a given mechanical or electrical machine. Itis appreciated that the given mechanical or electrical machine beingclassified may or may not be included in the population of electrical ormechanical machines from which data was accumulated, provided the givenmechanical or electrical machine shares at least one electrical ormechanical characteristic with members of the population of theelectrical or mechanical machines.

It is appreciated that the analysis performed on crowd-sourced dataacquired by system 400 does not necessarily include, nor is limited to,phase analysis of synchronous magnetic and vibration signals. Rather,the crowd-sourced data may be analyzed in accordance with any suitablemethods known in the art. Particularly preferably, crowd-sourcedmagnetic signals acquired by system 400 may be analyzed in order toderive a condition of motor 104.

Such analysis of magnetic signals based on crowd-sourcing may includecalculation of the machine slip frequency based on the machinesynchronous frequency as derived from the magnetic signal. The machineslip frequency may be useful for detecting changes in machine load or inidentifying a crawling fault.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection or prediction of eccentricity, whereinthe machine rotor and stator are not centered with respect to eachother. Eccentricity may be identified based on changes in the magneticpower spectrum. Particularly, during eccentricity the peak amplitude ofthe magnetic signal develops side bands, frequently accompanied byharmonics, thus allowing detection and evaluation of the severity ofthis fault. Detection of eccentricity may be enhanced by correlation ofmagnetic to vibration signals arising from the machine being monitored,due to the modified vibration patterns.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of a cracked or broken rotor bar basedon the presence of new frequency component peaks in the magnetic powerspectrum of a faulty machine in comparison to a non-faulty machine. Thenew frequency component peaks will be manifested as side bands ofs*f_(s,), where s is the slip frequency, as well as harmonics thereof.The presence and severity of cracked or broken rotor bars may beevaluated based on features of the magnetic power spectrum. Furthermore,the vibrations generated by a machine having a cracked or broken rotorbar also tend to change, due to the interaction between the stator fieldand the increased currents present in regions surrounding the cracked orbroken rotor bars. Detection of cracked or broken rotor bars may thus beenhanced by correlation of magnetic to vibration signals.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of electrical shorts in electricalmachine windings, since the shorted current flow produces an additionalmagnetic field component perpendicular to the field produced by themagnetic poles of the electrical machine. The shorted magnetic field istypically more prominent in the axial magnetic field and may be detectedbased on analysis of spectral peaks in the axial magnetic fieldspectrum, in order to identify an increase in the frequencies associatedwith a shorted field. Detection of winding electrical shorts may beenhanced by correlation of magnetic to vibration signals arising from amachine being monitored, due to the breakdown of the field symmetry.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of electrical discharge due toirregularities in magnetic circuits in the electrical machine. Suchelectrical discharge creates transient currents through the machineshafts, bearings and bearing supports and may cause bearing failure. Inorder to detect electrical discharge, a magnetic sensor such as magneticsensor 130 functioning in a low-power mode or low-power sensor 230 maybe operated, in order to provide at least near real time magnetic data.Anomalies and/or deviations in the magnetic time waveform may bedetected. Non-stationary magnetic fields with an appropriate time decaycharacteristic may be detected and classified as discharge, withseverity graded according to the discharge magnitude and repetitionrate. Filtering may be used to prevent masking of the discharge signal.In addition, the high frequency energy of the magnetic power spectrummay be calculated, which high frequency energy has been found toincrease in the case of non-stationary fields.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of mechanical unbalancing, wherein thecenter of mass of the electrical machine is not aligned with thegeometrical center thereof. This fault introduces a time varyingcomponent in the magnetic field, detectable by the monitoring system ofthe present invention.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of extremum operating conditions, suchas due to overloading, over-voltage or over-speeding of an electricalmachine. Extremum operating conditions may be detected based ondeviation of the magnetic signature of the machine from a baselinesignature associated with normal machine operation. By way of example,overloading and over-voltage may be detected based on increased magneticenergy and may be verified based on changes in additional operatingparameters, such as temperature or vibrations.

Analysis of magnetic signals based on crowd sourcing may additionally oralternatively include detection of problems due to machine controllers,in particular Variable Frequency Drive (VFD) controllers. Problems dueto such controllers typically create anomalies in the incoming currentprovided to the electrical machine being monitored, and hence in themachine generated magnetic field. These anomalies may be detected bysignal processing functionality included in the present invention, asdescribed herein, and used to identify faults in the controller. By wayof example, high VFD noise may give rise to a magnetic signal havingincreased noise in comparison to a baseline signal noise level. Thisallows detection of deterioration of the VFD prior to the occurrence ofsevere faults. Detection of problems in machine controllers may beenhanced by correlation of the magnetic signal to a vibration signal,since anomalies in the magnetic signal result in variation of thegenerated torque and hence in the vibration signature of the machine.

It is appreciated that the above-mentioned faults are provided by way ofexample only and that system 400 may be used to monitor and detect awide range of mechanical or electrical faults of electrical machines,particularly preferably based on the acquisition and analysis of atleast magnetic data using a crowd sourcing technique, which analysis mayor may not include phase analysis of synchronously acquired signals.

Reference is now made to FIG. 5A, which is a simplified flow chartillustrating signal processing functionality useful in a system of thetype shown in FIGS. 1-3; and to FIG. 5B, which is a simplified flowchart illustrating additional signal processing functionality useful ina system of the type shown in FIG. 4.

As seen in FIG. 5A, the signal processing functionality performed bysystem 100 preferably includes steps for calibration of a givenmechanical or electrical machine, as illustrated in a first calibrationcolumn 502, as well as steps for actual measurement of the mechanical orelectrical machine, as illustrated in a second measurement column 504.

Turning now to first calibration column 502, the machine under test(MUT) is preferably calibrated at a first calibration step 506. Firstcalibration step 506 preferably involves the measurement of at least oneoperating parameter of the MUT, such as magnetic field emission, andcalibration thereof in a variety of operational states of the machine.Such calibration may be used to establish a baseline signal,corresponding to normal operation of the MUT, which normal operation maybe healthy rather than faulty operation or legitimate rather thanillegitimate operation.

The calibrated output is preferably used to establish data patterns orfeatures associated with various machine conditions, as seen at a secondcalculation step 508. Such features may be thresholds based on one orboth of time domain and frequency domain spectral features of data ofthe MUT in the various calibrated operational states thereof. Suchfeatures may additionally or alternatively be machine-learning baseddata trends or models. These emission features may be used to build up adictionary of data features, as seen at third compilation step 510.

By way of example, the data features derived at second calculation step508 may be discrete magnetic field signal thresholds corresponding torespective operational states of the MUT. These discrete thresholds maybe unique to the particular MUT or may be standard thresholds found tobe applicable to a range of similar electrical machines.

Alternatively, the data features derived at second step 508 maycorrespond to models of signals, such as magnetic field signals,statistically correlated to respective operational states of the MUT,which models may be based on historical measurements of the magneticfield emission signal over time and between various operating conditionsof the MUT.

It is appreciated that first-third steps 506-510 shown in calibrationcolumn 502 may be carried out by one or more data processing modules 150in cooperation with server 160. Alternatively, depending on theparticular thresholds applied, first-third steps 506-510 may be carriedout by external, additional signal collection and processing modules andthe data pattern dictionary compiled at third step 510 stored at server160.

Turning now to second measurement column 504, operational parameter datagenerated by the MUT is acquired at a fourth step 512. By way ofexample, magnetic field emission data may be acquired by magnetic fieldemission sensor 130 in sensor module 110 and received therefrom by dataprocessing unit 150.

At a fifth step 514, data features are extracted from the acquired data.Feature extraction may include extraction of physical features of thedata. For example, in the case of magnetic field emission data, step 514may involve extraction of features used to represent the magneticsignal, such as principle components of the magnetic waveform and thepower spectrum thereof, total magnetic signal energy, magnetic energywithin defined time frames, magnetic energy within defined frequencybins and fluctuations in magnetic energy. Feature extraction may alsoinclude extraction of statistical features of the magnetic fieldemission, including statistical moments and correlations and cumulantsof operation parameter signals, signal entropy and signal noise, as wellas extraction of signal integrity features such as signal span andstationarity. Feature extraction at step 514 may also involve extractionof features indicative of the presence and/or severity of faults, as isfurther detailed henceforth.

At a sixth step 516 and seventh step 518, features extracted at fifthstep 514 are respectively validated by and compared to features of datapatterns held in the dictionary built up at third step 510.Particularly, features extracted from the received data may be comparedto features of the baseline operating parameter signal, such thatvalidation of the features takes into account the baseline signalassociated with normal operation of the MUT. Validated features may befed back to the dictionary, thereby further building up the MUTdictionary. As a result of such feedback, the reference data patternsheld in the MUT dictionary may be dynamically changing patterns. Featurevalidation may include comparing patterns of change over time of thesignal sensed from the MUT to patterns of change over time of historicalsignals associated with past failures of the MUT or of machines similarto the MUT.

Extracted features may be within predefined or machine-learned limits,allowing classification of the state of the MUT, as seen at an eighthstep 520, leading to generation of a machine status at a ninth step 522.The status may indicate deterioration of the MUT and predict impendingfailure prior to the occurrence of operational failure. Furthermore, thestatus may indicate the particular nature of the operational failurelikely to occur. Alternatively, extracted features may deviate from thepre-defined or machine-learned baseline signals, indicating anomalousoperation of the MUT as seen at a tenth step 524. Identification ofmalfunction of the MUT may result in the generation of a malfunctionalert and/or feedback to the MUT, for example by way of control module180.

It is understood that calibration of the MUT and compilation of the MUTdictionary may involve the measurement and calibration of signals fromthe MUT itself. Alternatively, calibration and compilation of the MUTdictionary may involve the measurement and calibration of signals from apopulation of similar machines resembling but not necessarily identicalto the MUT, using machine learning algorithms in combination with acrowd-sourcing approach as illustrated in FIG. 4 and further detailedwith respect to FIG. 5B.

As seen in FIG. 5B, members of a population of machines used forcalibration measurements may be devices 1-N, selected based on having atleast one electrical or mechanical characteristic in common with the MUTsuch as, by way of example, a common part or model number. Thepopulation of electrical or mechanical machines based on which a givenmachine may be calibrated may or may not include the machine itself.

In the crowd-sourcing approach illustrated in FIG. 5B, data is acquiredfrom each of devices 1-N at a data acquisition step 530. Features arethen extracted from the data acquired for each device at a featureacquisition step 532. The extracted features are preferably validated ata feature validation step 534. Validated features are preferably sentfor further analysis at an analysis step 536. Analysis step 536 mayinvolve analysis of extracted features by human experts, as seen at ananalysis step 538. The performance of the analysis results in ananalysis output, as seen at an analysis output step 540, which output ispreferably fed back into the data feature dictionary at an updating step542.

The dictionary compiled at third step 510 of FIG. 5A thus may compriseor be augmented by data patterns identified based on statistical modelsof signals, such as magnetic field emission signals, gleaned frommeasurements of signals of machines sharing mechanical or electricalcharacteristics with the MUT but not necessarily being identicalthereto, based on a crowd-sourcing approach as illustrated in FIG. 5B.The incorporation of data patterns based on related machines in the datapatterns dictionary allows the compilation of a richer, more widelyapplicable dictionary having a higher confidence level associatedtherewith.

In the case that patterns of change over time of the operationalparameter signal sensed from the MUT are found to be similar to patternsof change over time of historical operational parameter signalsassociated with past failures of the MUT or of electrical machinessimilar to the MUT, an output may be generated by control module 180comprising a prediction of impending failure of the MUT based onsimilarities between patterns of change over time of the presentmeasured operational parameter signal and patterns of change over timeof historical operational parameter signals. Extracted features found todeviate from the pre-defined or machine-learned limits may also be fedback to the data feature dictionary in order to update the data featuredictionary.

By way of example, in the case that a system such as system 100 or 400is used in detecting anomalous operating states as means of identifyingundesirable malicious interference in the operation of motor 104 ordevice 106, data processing unit 150 may receive measured magnetic fieldemission signals and extract features therefrom. Data processing unit150 in optional cooperation with server 160 may furthermore identify anoperating state of motor 104 or device 106 based on the extractedfeatures and compare the identified operating state to historicaloperating states of at least one reference machine having at least oneshared mechanical or electrical characteristic with the monitoredmachine. It is appreciated that the historical operating states may ormay not include historical operating states of the monitored machineitself.

Additionally, data processing unit 150 and/or server 160 may provide anoutput based on the comparing, the output being indicative at least ofwhether the identified operating state is anomalous with respect to thehistorical operating states. As detailed above, an anomalous operatingstate may be caused, for example, by security breaches in the operationof motor 104 or device 106 or errors in the operating code thereof.

In the case that feature extraction and validation involves machinelearning, a possible input of machine learning algorithms is anormalized set of various feature parameters as described above and thedesired output may be, for example, predicted time-to-failure of theMUT. Training of such machine learning algorithms is preferablyperformed by providing historical examples of data relating to failuresand faults. During an evaluation stage, each time data is recorded fromthe operational parameter sensors in sensor module 110 relevantparameters are calculated on the data, which parameters may beidentified as p1, p2 etc, as indicated in equation (1) below.

p{p ₁ ,p ₂ , . . . ,p _(N)}  (1)

These parameters may include, for example, peak amplitude, peakfrequency, time waveform and total energy. The data may then benormalized using Z-score transformation relative to a historicalbaseline, in accordance with equation (2) below.

z={z ₁ ,z ₂ , . . . ,z _(N)}  (2)

where

z _(i)=(p _(i)−μ_(i))/σ_(i)

and μ_(i) is mean of parameter p_(i) under similar operating conditionsin the same or similar machine. In a more general multivariate case:

z=(p−μ)^(τ)Σ⁻¹(p−μ)

where μ is a mean of parameter vector p known from historical data, andΣ is a covariance matrix calculated from historical data as well. Theoutput of the system is expected time-to-failure (T_(ttf)).

During a training stage, various parameters are calculated usinghistorical data as the input to the algorithm and time-to-failureprovided as a target output. In this formulation the task is a simpleregression:

T _(ttf) =f(z,C)

where C represents parameters of the learning system calculated fromhistorical data on the same or similar devices. One of the simplestsolutions is using linear or logistic regression. In a linear case:

T_(ttf) = z ⋅ C = ∑z_(i)C_(i)??indicates text missing or illegible when filed

It is understood that the forgoing corresponds to one possibleimplementation of machine learning algorithms useful in the presentinvention, and that the use of any appropriate machine learningalgorithm may be possible.

It is appreciated that the signal processing steps illustrated in FIGS.5A and 5B are not necessarily carried out in the order shown anddescribed and that various steps may be interchanged with other steps.Furthermore, it is appreciated that the signal processing steps mayinclude additional steps not described herein, as may be known in theart.

Performance of signal analysis, in accordance with algorithms outlinedhereinabove or in accordance with any other signal processing algorithmsknown in the art, may be useful in deriving a condition of the at leastone machine 102 being monitored. Examples of various machine conditionsidentifiable based on analysis by data sensor module 150 and/or server160 of signal output by at least one sensor module 110 are providedhereinbelow with reference to FIGS. 6-12. More specifically, examples ofvarious asynchronous electrical machine conditions identifiable based onanalysis of magnetic field emission signals synchronously sensed along aplurality of channels, optionally in synchronous combination withvibration signals, are provided. It is appreciated, however, that theparticular faults described hereinbelow are exemplary only and thatsystems and methods of the present invention may be used to derive awide range of electrical and mechanical machine conditions.

Reference is now made to FIG. 6, which is a simplified graph displayingmagnetic field data synchronously acquired along multiple channels by asystem of the types illustrated in FIGS. 1-4, as measured for a properlyoperating asynchronous electrical machine.

As seen in FIG. 6, a first graph 600 and a second graph 602 areprovided, both of which first and second graphs 600 and 602 displaymagnetic field emission signals as synchronously measured along twosignal channels by two magnetic field emission sensors 130 associatedwith asynchronous motor 104. The signal denoted B_(r) ¹ corresponds tothe radial magnetic field signal as measured by a first magnetic sensor130, for example included in sensor module 112 and the signal denotedB_(r) ² corresponds to the radial magnetic field signal as measured by asecond magnetic sensor 130, for example included in sensor module 116.It is appreciated that the two magnetic field emission sensors 130 mayalternatively be included in a single sensor module 110. It isunderstood that the radial magnetic field is measured in this case byway of example, due to the typical dominance thereof.

First graph 600 displays magnetic field data as measured by two magneticsensors 130 located in the same orientation with respect to motor 104,for example, mounted at two ends of motor 104. As seen in graph 600,magnetic signals B_(r) ¹ and B_(r) ² coincide in phase. The amplitude ofsignals B_(r) ¹ and B_(r) ² is seen to differ slightly, due to thediffering distance between each sensor module 112 and 116 and themagnetic pole within motor 104.

Second graph 602 displays magnetic field data as measured by twomagnetic sensors 130 located on the same plane of motor 104 in the casethat one sensor module is rotated by π/2 with respect to the othersensor module. The difference in orientation of the sensor modulescreates a corresponding constant phase difference of π/2 betweenmagnetic signals B_(r) ¹ and B_(r) ² in the case of a healthy, properlyoperating motor 104.

It is appreciated that coincident phase of magnetic signals B_(r) ¹ andB_(r) ² in the case of graph 600 and the constant phase offset ofmagnetic signals B_(r) ¹ and B_(r) ² in the case of graph 602 indicatemotor 104 to be in a properly operating, healthy state. Should motor 104be in a faulty or impending faulty state, the phase relationship ofmagnetic signals B_(r) ¹ and B_(r) ² would be disrupted and theamplitude of magnetic signals B_(r) ¹ and B_(r) ² would change. Featuresof synchronous magnetic data of the type displayed in FIG. 6, includingsignal phase and amplitude, may thus be used to ascertain that machine104 is in a properly operating, healthy state. It is appreciated thatsuch analysis is enabled by the synchronous sampling of magnetic signalsB_(r) ¹ and B_(r) ².

Orbit plots of magnetic signals B_(r) ¹ and B_(r) ² corresponding to thesensor arrangement giving rise to data of graphs 600 and 602 aredisplayed in FIGS. 7A-7C.

As seen in FIG. 7A, in the case of two magnetic sensors 130 located inthe same orientation with respect to motor 104 and therefore with nophase difference therebetween, magnetic signals B_(r) ¹ and B_(r) ² arelinearly related when motor 104 operates in a healthy manner. Thiscorresponds to the data presented in graph 600.

As seen in FIG. 7B, in the case of two magnetic sensors 130 located onthe same plane of motor 104 where one sensor module is rotated by π/2with respect to the other sensor module, thereby creating acorresponding constant phase difference of π/2, magnetic signals B_(r) ¹and B_(r) ² have an elliptical relationship when motor 104 operates inhealthy manner. This corresponds to the data presented in graph 602. Itis appreciated that should magnetic signals B_(r) ¹ and B_(r) ² be ofthe same amplitude, a circular rather than elliptical relationship wouldexist therebetween in a healthy machine state.

As seen in FIG. 7C, for the same mounting positions of magnetic sensorscorresponding to graph 600 and FIG. 7A, but when motor 104 is in afaulty state, the linear relationship between magnetic signals B_(r) ¹and B_(r) ² is disrupted and the two field frequencies of B_(r) ¹ andB_(r) ² differ by approximately 5%. This gives rise to the disruptedquasi-elliptical orbit plot of FIG. 7C. The loss of the ellipticalrelationship between B_(r) ¹ and B_(r) ² indicates a problem in one ofthe electrical phases of the incoming current. Feature of the orbit plotof FIG. 7C, such as phase and magnitude which may be extracted from thegraph of FIG. 7C, may be used to derive the particular nature andseverity of the fault. Based on this, faults in a VFD or controller maybe detected and analyzed.

It is appreciated that the data presented in FIG. 7C corresponds to datacollected over only a relatively short time and thus small number ofmagnetic signal cycles. As the duration of measurement is increased, thequasi-ellipse of FIG. 7C would be expected to become increasinglydistorted.

It is further appreciated that such analysis is enabled by thesynchronous sampling of magnetic signals B_(r) ¹ and B_(r) ². Shouldmagnetic signals B_(r) ¹ and B_(r) ² not be synchronously sampled, theconstruction of orbit plots, such as those shown in FIGS. 7A-7C, wouldnot be possible due to the phase difference between the various magneticsignals due to the sampling regime. It is understood that such phaseanalysis is highly advantageous in allowing earlier detection of faultsthan would be possible by other signal analysis methods such as, forexample, FFT.

Reference is now made to FIG. 8, which is a simplified first and secondgraph displaying magnetic and vibrational data synchronously acquiredalong multiple channels by a system of any of the types illustrated inFIGS. 1-4, as measured for a properly operating asynchronous electricalmachine.

As seen in FIG. 8, a first graph 800 and a second graph 802 areprovided, both of which first and second graphs 800 and 802 displayvibration signals as synchronously measured along three signal channelsby three vibration sensors in addition to magnetic signals assynchronously measured by a single magnetic sensor, all of whichvibration and magnetic sensors are associated with asynchronous motor104 connected to device 106, which in this case is embodied as a pump.

The signal denoted B_(r) corresponds to the radial magnetic field signalas measured by magnetic sensor 130, for example included in sensormodule 112; the signal denoted a_(r) corresponds to the radial(vibration) acceleration signal, for example as measured by radialtri-axial vibration sensor 220; the signal denoted a_(θ) corresponds tothe theta direction (vibration) acceleration signal, for example asmeasured by theta-direction vibration sensor 222; and the signal denoteda_(z) corresponds to the z direction (vibration) acceleration signal,for example as measured by z-direction vibration sensor 224. It isunderstood that the radial magnetic field is measured in this case byway of example, due to the typical dominance thereof. It is appreciatedthat the data displayed in graphs 800 and 802 thus may correspond todata measured by sensors included in a single sensor module 110.

First graph 800 displays synchronous magnetic and vibration data,filtered in order to show the rpm frequency. The rpm frequency is thefrequency of the waves and is given by the reciprocal of the wave cycleperiod. As seen in graph 800, the magnetic and vibration data issynchronized. Second graph 802 displays synchronous magnetic and radialvibration acceleration data only. As best appreciated from considerationof second graph 802, the magnetic signal B_(r) varies at a greater ratethan the radial (vibration) acceleration signal a_(r), indicating thatthe magnetic field speed is greater than the vibration rotation speed.This is as would be expected to be the case for an induction motor,which is an asynchronous AC machine. The difference between the magneticand vibration signals corresponds to the slip frequency of the inductionmotor.

Features of synchronous magnetic and vibration data of the typedisplayed in FIG. 8, including signal phase and amplitude, may be usedto ascertain that motor 104 is in a properly operating, healthy state.It is appreciated that such analysis is enabled by the synchronoussampling of magnetic and vibration signals.

A phase plot for data acquired using the sensor arrangement giving riseto the data of FIG. 8 is displayed in FIG. 9. Reference is now made toFIG. 9, which is a phase plot based on data acquired by synchronousmagnetic and vibrational sampling along multiple channels for a properlyand improperly operating asynchronous electrical machine, as acquired bya system of any of the types illustrated in FIGS. 1-4.

As seen in FIG. 9, the relative phase of the relative magnetic andvibration signals θ as measured by a single sensor module 110 is plottedagainst the sum of the magnetic and vibration signal energies, asrepresented by the radius R of the orbit plot, for both a healthy andnon-healthy asynchronous motor 104. The relative phase may be defined asθ=arctan (magnetic signal/vibration signal). The radius R may be definedas R=(B²+V²)^(0.5), where B is the magnetic field and V is thevibration, both measured simultaneously.

In the case of asynchronous motor 104 being in a faulty, improperlyoperating state, the relative phases are more broadly distributed thanfor a healthy properly operating state, leading to an increased width ofpeak seen in a region 902 in the case of an unhealthy motor. This mayindicate a mechanical fault in motor 104, such as looseness or a bearingfault. Various statistical measures, such as a comparison of moments ofthe probability density function, may be used in order to evaluate phaseplots such as those shown in FIG. 9, in order to derive the condition,including type and severity of fault, of motor 104.

Reference is now made to FIG. 10, which is a simplified graph displayingmagnetic and vibrational data synchronously acquired along multiplechannels by a system of any of the types illustrated in FIGS. 1-4, asmeasured for a properly operating asynchronous electrical machine.

As seen in FIG. 10, a graph 1000 is provided displaying synchronousvibration measurements a_(r) ¹ and a_(r) ² for motor 104 and device 106as measured by two radial vibration sensors, such as one vibrationsensor 140 in sensor module 112 mounted on motor 104 and one vibrationsensor 140 in sensor module 118 mounted on device 106, and synchronousmagnetic field emission measurements B_(r) ¹ and B_(r) ² for motor 104as measured by two magnetic sensors, such as sensors 130 in sensormodules 112 and 116. It is appreciated that that only the radialcomponents of the measured vibrations are included, for the purpose ofclarity, although θ and z-direction vibration signal components may alsobe measured by such as system.

As appreciated from consideration of graph 1000, the vibration signalsare generally synchronized, although a small phase offset is seenbetween the signals due to real-life influences on the data collected,such as due to the transfer function of motor 104 and device 106. Thedata displayed in FIG. 10 is filtered in order to show the rpm.

It is appreciated that the almost coincident phase of magnetic signalsB_(r) ¹ and B_(r) ² and vibration signals indicate motor 104 and device106 to be in a properly operating, healthy state. Should motor 104and/or device 106 be in a faulty or impending faulty state, the phaserelationship of magnetic signals B_(r) ¹ and B_(r) ² and of thevibration signals would be disrupted and the amplitude of magnetic andvibration signals would change. Features of synchronous magnetic andvibration data of the type displayed in FIG. 10, including signal phaseand amplitude, may thus be used to ascertain that motor 104 and device106 is in a properly operating, healthy state. It is appreciated thatsuch analysis is enabled by the synchronous sampling of magnetic andvibrational signals.

It is noteworthy that graph 1000 displays synchronized magnetic andvibration data acquired by sensor modules located on connectedelectrical and mechanical machines, in this case embodied asasynchronous motor 104 and pump 106. This illustrates the utility ofsynchronous signal acquisition and consequent phase analysis of signalsacquired by multiple sensor modules, such as multiple sensor modules110, located on different machines rather than a single machine,operation of which multiple sensor modules 110 may be synchronized bydata acquisition module 150.

A phase plot for data acquired using the sensor arrangement giving riseto the data of FIG. 10 is displayed in FIG. 11. Reference is now made toFIG. 11, which is a phase plot based on data acquired by synchronousmagnetic and vibrational sampling along multiple channels for a properlyand improperly operating asynchronous electrical machine, as acquired bya system of any of the types illustrated in FIGS. 1-4.

As seen in FIG. 11, the relative phase θ between the radial magnetic andvibration signals as measured by two sensor modules 110 is plottedagainst the sum of the magnetic and vibration signal energies, asrepresented by the radius R of the orbit plot. In the case ofasynchronous motor 104 and pump 106 being in a faulty, improperlyoperating state, the energy peak is rotated by an angle of approximatelyπ relative to the energy peak of the healthy operating state. This mayindicate a misalignment between motor 104 and pump 106. Variousstatistical measures, such as a comparison of probability densityfunction moments, may be used in order to evaluate phase plots such asthose shown in FIG. 11, in order to derive the condition, including typeand severity of fault, of motor 104 and/or device 106 connected thereto.

The same fault identified based on the phase plot of FIG. 11 may also beindicated by an orbit plot of the type shown in FIG. 12. As seen in afirst orbit plot 1202 in FIG. 12, when motor 104 and device 106 are in ahealthy operating state, the synchronous vibration and magnetic signalsare seen to have a well-defined phase relationship. Misalignment betweenmotor 104 and pump 106 which gives rise to a phase difference of π isseen to lead to a rotation of the phase relationship, as seen in asecond orbit plot 1204 representing a faulty condition of motor 104 andpump 106.

It is appreciated that in this case, the well-defined phase relationshipbetween the vibration and magnetic signals is maintained during faultyoperation. However, the direction of the phase relationship rotates,thus indicating the presence of a fault such as misalignment between themotor 104 and pump 106. It is further appreciated that the detection ofa fault such as misalignment between motor and pump based on synchronousmagnetic and vibration signals is more accurate than detection of such afault based on vibration signals alone, due to the reduced influence ofthe machine transfer function on magnetic signals.

Reference is now made to FIG. 13, which is a simplified schematicillustration of a system for monitoring a machine, constructed andoperative in accordance with another preferred embodiment of the presentinvention.

As seen in FIG. 13, there is provided a system 1300 for monitoring,preferably although not necessarily continuously, operation of at leastone machine 1302. Machine 1302 may be one or more of a mechanicalmachine and/or an electrical machine, which electrical machine may be anasynchronous electrical machine, such as an asynchronous motor orgenerator, or a synchronous electrical machine such as a synchronousmotor or generator. Machine 102 may furthermore be embodied as analternating current (AC) or direct current (DC) electrical machine.Here, by way of example, at least one machine 1302 is seen to beembodied as a synchronous generator 1304 cooperatively coupled to anengine 1306.

It is appreciated that the term synchronous as used herein to refer to asynchronous electrical machine is to be distinguished from the termsynchronous as used herein to refer to synchronous signal sampling. Asis well known in the art, a synchronous electrical machine is anelectrical machine in which the shaft speed is identical to the rotationspeed of the magnetic field inside the electrical machine. As describedhereinabove, synchronous signal sampling as used herein refers to thesensing of signals by two or more sensors when the sampling differencein time between the sensors is of the order of less than or equal toabout 0.01/F_(s) where F_(s) is the sensor sampling frequency.

Operation of generator 1304 and engine 1306 connected thereto ispreferably monitored by at least one sensor module, here embodied assensor module 110, for sensing operating parameters of at least one ofgenerator 1304 and machine 1306. Here, by way of example, at least onesensor module 110 is seen to be embodied as first sensor module 112 andsecond sensor module 116 mounted on generator 1304 and third sensormodule 118 and a fourth sensor module 1310 mounted on engine 1306. It isappreciated, however, that system 1300 may include a fewer or greaternumber of sensor modules 110 distributed between generator 1304 andengine 1306.

Sensor modules 112, 116, 118, 1310 are preferably mounted on variouslocations of the frames of generator 1304 and engine 1306, for examplein close proximity to machine bearings. Multiple ones of sensor modulesassociated with a particular machine, such as sensor modules 112 and 116associated with generator 1304, may be mounted at mutually similarorientations or may be mounted at different orientations depending onthe nature of the operating parameter to be sensed and monitoredthereby. Sensor modules 110 may be in physical contact with the machinemonitored thereby, as here illustrated to be the case for sensor modules112, 116, 118, 1310 with respect to generator 1304 and engine 1306.Alternatively, sensor modules 110 may be physically offset from themachine monitored thereby, provided that sensor modules 110 arepositioned so as to be capable of sensing at least one operatingparameter of the machine to be monitored thereby.

Each sensor module 110 preferably comprises at least one sensor, andmore preferably a collection of sensors, for sensing at least oneoperating parameter of at least one of generator 1304 and engine 1306.As seen most clearly at an enlargement 1320, sensor module 110 maycomprise at least one magnetic sensor 130 for sensing magnetic fieldsemitted by generator 1304. Sensor module 110 may additionally compriseat least one vibration sensor 140 for sensing vibrations arising fromgenerator 1304, engine 1306 or both. Sensor module 110 may furtherincludes additional sensors for sensing a variety of operationalparameters including, but not limited to, temperature and acousticemission, as is detailed henceforth.

It is a particular feature of a preferred embodiment of the presentinvention that in the case that generator 1304 is monitored by at leasttwo magnetic sensors 130, the at least two magnetic sensors 130 arepreferably operative to mutually synchronously sense magnetic fieldsemitted by the at least one machine being monitored thereby, hereembodied as generator 1304, along a corresponding plurality of signalchannels. The two or more magnetic sensors 130 may be included in asingle sensor module 110 or in multiple individual ones of sensor module110.

It is a further particular feature of a preferred embodiment of thepresent invention that in the case that generator 1304 is monitored byat least one magnetic sensor 130 and at least one vibration sensor 140,the at least one magnetic sensor 130 and at least one vibration sensor140 are operative to synchronously sense magnetic fields and vibrationsemitted by the at least one machine being monitored thereby, hereembodied as at least one of generator 1304 and engine 1306. The at leastone magnetic sensor 130 and at least one vibration sensor 140 may bothbe included in a single sensor module 110 or in separate ones of sensormodule 110.

Further details pertaining to the preferable structure and operation ofsensor module 110 are provided hereinabove with reference to FIGS. 1 and2A-2E.

Sensor module 110 preferably includes communication functionality and ispreferably adapted for wireless communication with at least one dataprocessing module 150. Preferably, a single data processing module 150is operative to receive data in the form of signals from multiple onesof sensor module 110 mounted on at least one machine, here embodied, byway of example as four sensor modules 112, 116, 118 and 1310 mounted ongenerator 1304 and engine 1306 and all in communication with dataprocessing module 150.

Data processing module 150 may be located remotely from the varioussensor modules 110 in communication therewith, provided that dataprocessing module 150 is capable of receiving signals from the varioussensor modules 110. By way of example, data processing module 150 may bemounted on the wall of a room in which generator 1304 and engine 1306are located. Further details pertaining to the preferable structure andoperation of data processing module 150 are provided hereinabove withreference to FIGS. 1 and 3A-3C.

At least a portion of the data received at data processing module 150 ispreferably transmitted thereby to server 160, typically on the cloud,for processing. Server 160 is preferably operative to receive data fromat least one data processing module 150 and to analyze the data inaccordance with automatic algorithms, preferably including machinelearning algorithms. Analysis of data by server 160 may includeprocessing of information in a cloud server as described in U.S. Pat.No. 9,835,594, filed Oct. 22, 2012 and entitled AUTOMATIC MECHANICALSYSTEM DIAGNOSIS, the disclosure of which is hereby incorporated byreference.

Analysis of data by server 160 may include the execution of algorithmsfor detection of a condition of generator 1304 and/or engine 1306,including detection or prediction of mechanical and electrical faults,efficiency analysis and analysis of degradation of performance ofgenerator 1304 and/or engine 1306. Furthermore, analysis of data byserver 160 may be used to identify possible security breaches in controlof generator 1304 and/or engine 1306, due for example to hacking orother malicious activities directed against generator 1304 and/or engine1306 via computerized controls thereof.

Further details pertaining to the processing steps performed by server160 are provided hereinabove with reference to FIGS. 1, 5A and 5B.

At least one of data processing module 150 and server 160 preferablyprovides an output 1370 based on the analysis performed thereby, whichoutput 1370 preferably includes at least an indication of a condition ofthe at least one machine being monitored, such as generator 1304 and/orengine 1306.

It is appreciated that data processing module 150 in combination withserver 160 thus preferably constitutes a particularly preferredembodiment of signal analyzer 172, receiving at least a portion of thesignals sensed by sensor module 110, performing analysis of the signalsand providing an output based on the analysis, which output preferablyincludes at least an indication of a condition of the at least onemachine being monitored.

System 1300 further preferably includes control module 180, receivingthe indication of a condition of the machine being monitored from server160 and/or data processing module 150. Control module 180 may be anycomputing device, such as a computer 182 or personal communicationdevice 184 illustrated herein. Control module 180 preferably initiatesat least one of a repair event on the at least one machine beingmonitored, an adjustment to a maintenance schedule of the at least onemachine and an adjustment to an operating parameter of the at least onemachine based on the indication provided thereto.

Here, by way of example, magnetic signal phase analysis performed byserver 160 and/or data processing unit 150 may automatically yield anindication of an actual or incipient fault in generator 1304 based on aphase analysis plot 1380. Control module 180 may receive indication ofthe fault predicted or detected and repair, deactivate or otherwiseadjust generator 1304 responsively. It is appreciated that the controlactions performed by control module 180 thus preferably serve to improvethe efficacy of the at least one machine being monitored.

Examples of various machine conditions identifiable based on analysis bydata sensor module 150 and/or server 160 of signal output by at leastone sensor module 110 in system 1300 are provided hereinbelow withreference to FIGS. 14-17. More specifically, examples of varioussynchronous electrical machine conditions identifiable based on analysisof magnetic field emission signals synchronously sensed along aplurality of channels, optionally in synchronous combination withvibration signals, are provided. It is appreciated, however, that theparticular faults described hereinbelow are exemplary only and thatsystems and methods of the present invention may be used to derive awide range of electrical and mechanical machine conditions.

Reference is now made to FIG. 14, which is a simplified first and secondgraph displaying magnetic and vibrational data synchronously acquiredalong multiple channels by a system of the type illustrated in FIG. 13,as measured for a properly operating synchronous electrical machine.

As seen in FIG. 14, a first graph 1400 and a second graph 1402 areprovided, both of which first and second graphs 1400 and 1402 displayvibration signals as synchronously measured by three vibration sensorsin addition to magnetic signals as synchronously measured by a singlemagnetic sensor, all of which vibration and magnetic sensors areassociated with synchronous generator 1304 connected to engine 1306, orany other type of synchronous electrical machine.

The signal denoted B_(r) corresponds to the radial magnetic field signalas measured by magnetic sensor 130, for example included in sensormodule 112; the signal denoted a_(r) corresponds to the radial vibrationacceleration signal, for example as measured by radial tri-axialvibration sensor 220; the signal denoted a_(θ) corresponds to the thetadirection vibration acceleration signal, for example as measured bytheta-direction vibration sensor 222; and the signal denoted a_(z)corresponds to the z direction vibration acceleration signal, forexample as measured by z-direction vibration sensor 224. It isunderstood that the radial magnetic field is measured in this case byway of example, due to the typical dominance thereof. It is appreciatedthat the data displayed in graphs 1400 and 1402 thus may correspond todata measured by sensors included in a single sensor module 110.

First graph 1400 displays synchronous magnetic and vibration data,filtered in order to show the rpm frequency. The rpm frequency is thefrequency of the waves and is given by the reciprocal of the wave cycleperiod. As seen in graph 1400, the magnetic and vibration data issynchronized. Second graph 1402 displays synchronous magnetic and radialvibration acceleration data only. As best appreciated from considerationof second graph 1402, the magnetic signal B_(r) varies at the same rateas the radial vibration acceleration signal a_(r), indicating that themagnetic field speed is identical to the vibration rotation speed. Thisis as would be expected to be the case for a synchronous generator.

It is appreciated that coincident phase of vibration signals a_(r) andmagnetic signal B_(r) indicates synchronous generator 1304 to be in aproperly operating, healthy state. Should generator 1304 be in a faultyor impending faulty state, the phase relationship between both thevibration signals and the magnetic signals would be disrupted and theamplitude of one or both of the vibration and magnetic signals wouldchange in correspondence with the severity of the fault. Features ofsynchronous magnetic and vibration data of the type displayed in FIG.14, including signal phase and amplitude, may thus be used to ascertainthat generator 1304 is in a properly operating, healthy state. It isappreciated that such analysis is enabled by the synchronous sampling ofmagnetic and vibration signals.

Reference is now made to FIGS. 15A and 15B, which are simplified graphsdisplaying magnetic field data synchronously acquired along multiplechannels by a system of the type illustrated in FIG. 13, as respectivelymeasured for a properly operating and improperly operating synchronouselectrical machine.

As seen in FIGS. 15A and 15B, a first graph 1500 and a second graph 1502are provided, both of which first and second graphs 1500 and 1502display magnetic field emission signals as synchronously measured alongtwo signal channels by two magnetic field emission sensors 130associated with generator 1304. The signal denoted B_(r) ¹ correspondsto the radial magnetic field signal as measured by a first magneticsensor 130, for example included in sensor module 112 and the signaldenoted B_(r) ² corresponds to the radial magnetic field signal asmeasured by a second magnetic sensor 130, for example included in sensormodule 116. It is appreciated that the two magnetic field emissionsensors 130 may alternatively be included in a single sensor module 110.It is understood that the radial magnetic field is measured in this caseby way of example, due to the typical dominance thereof.

As seen in graph 1500 of FIG. 15A, in the case of generator 1304operating properly, magnetic signals B_(r) ¹ and B_(r) ² are of equalphase. In graph 1500, magnetic signals B_(r) ¹ and B_(r) ² are alsoshown to be of equal amplitude, although it is appreciated that this isnot necessarily the case, since amplitude will differ depending on thedistance of the sensor from the magnetic poll.

In the case of generator 1304 being in an unhealthy or improperlyoperating state, an amplitude variation between B_(r) ¹ and B_(r) ² iscreated, as seen in graph 1502 of FIG. 15B. Such amplitude variationbetween the magnetic field signals of two magnetic sensors on generator1304 is indicative of a mechanical fault in generator 1304, such asunbalancing on the vertical axis of generator 1304. The amplitude of themagnetic signal B_(r) ¹ may be expressed as A (B_(r) ¹)=B¹ sin(ωt) andthe amplitude of the magnetic signal B_(r) ² may be expressed as A(B_(r) ²)=B² sin(ωt+φ), wherein φ is the phase shift of B_(r) ² withrespect to B_(r) ¹. In this case, φ is equal to π and is responsible forthe variation in amplitude between B_(r) ¹ and B_(r) ² seen in graph1502 of FIG. 15B, as a result of unbalancing.

The mechanical problem indicated by data displayed in FIG. 15B mayadditionally or alternatively be derived based on synchronous magneticand vibration monitoring of generator 1304, data for which is displayedin FIGS. 16A and 16B. In this case, generator 1304 is preferablysynchronously monitored by two magnetic sensors 130 providing magneticfield emission signals B_(r) ¹ and B_(r) ² and two vibration sensors 140providing vibration acceleration signals a_(θ1) and a_(θ2). Preferably,pairs of magnetic and vibration sensors 130, 140 are housed in twosensors modules 110 located at either end of a shaft of generator 1304.

As seen in FIG. 16A, the synchronous magnetic field emission signals areof coincident phase and amplitude and therefore do not give anindication of generator 1304 being in a faulty state. However, as seenin FIG. 16B, the vibration signals exhibit an amplitude variation on thehorizontal machine axis that differs by a phase shift of π, as explainedhereinabove with reference to FIG. 15B. The presence of such a phaseshift indicates unbalancing to be present on the horizontal axis ofgenerator 1304.

Reference is now made to FIG. 17, which is an orbit plot for twomagnetic field emission signals synchronously measured along two signalchannels, as measured for a properly and improperly operatingsynchronous electrical machine.

As seen in FIG. 17, a graph 1700 is provided displaying magnetic fielddata as measured by two magnetic field emission sensors 130 located onthe same plane of generator 1304, but mutually rotated with respect toeach other by π/2. The magnitude of the magnetic field as a function ofangle inside the generator 1304 airgap is plotted in graph 1700. Asappreciated from a comparison of the magnetic field data for a properlyoperating machine to the magnetic field data for an improperly operatingmachine, the orbit plot of the improperly machine has an ellipticalshape in contrast to the circular orbit plot associated with a properlyoperating machine. The elliptical shape corresponding to the machine inan unhealthy operating state is indicative of a negative phase sequence,which negative phase sequence results in unbalanced rotation of themagnetic field. Such unbalanced rotation would lead to enhanced machinevibrations and thereby cause machine deterioration.

Features of synchronous magnetic data of the type displayed in FIG. 17,including signal phase and amplitude, may thus be used to ascertain thecondition of generator 1304 and/or engine 1306. It is appreciated thatsuch analysis is enabled by the synchronous sampling of magneticsignals, thereby facilitating the performance of phase analysis thereon.

It is appreciated that the machine condition derived based on analysisof signals monitored by sensor modules 110 in accordance withembodiments of the present invention is not limited detection of faultsin machine operation. Rather, the machine condition derived may alsoinclude derivation of the machine energy consumption, machine slip,machine working states and downtimes, machine load and machineefficiency, based on one or more of which control module 180 may adjustoperating metrics of the machine 102 being monitored.

Reference is now made to FIG. 18, which displays data showing trends inenergy consumption for an electrical machine, as acquired by a system ofany of the types illustrated in FIGS. 1, 4 and 13; and to FIG. 19, whichis a simplified graph displaying data showing trends in efficiency foran electrical machine, as acquired by a system of any of the typesillustrated in FIGS. 1, 4 and 13.

As seen in FIG. 18, energy dissipation in a machine being monitoredaccording to embodiments of the present invention may be measured bymultiple sensors, such as magnetic, temperature and vibration sensorsincluded in one or more sensor modules 110 associated with a mechanicalor electrical machine, such as a motor or generator.

As seen in FIG. 19, machine efficiency may be calculated by comparingthe incoming power supplied to the machine to the power being dissipatedby the machine. Incoming power to the machine may be calculated based onthe magnetic field signal sensed by at least one magnetic sensor 130,since the magnetic power may be assumed to be proportional to theincoming current driving the machine. The specific relationship betweenthe incoming power and magnetic power may be calibrated in order to takeinto account power losses due, for example, to eddy currents andattenuation created by the machine shielding.

Various methods may be used for ascertaining machine efficiency. In onepossible method, in accordance with a preferred embodiment of thepresent invention, the acceleration generated by the moving parts insidethe machine being monitored may be measured and translated intovibration energy, the machine temperature may be measured and translatedinto heat, and stray magnetic fields outside the machine may be measuredand translated into wasted potential energy. Furthermore, the magneticsignal, which is proportional to the incoming power, may be measured andmachine efficiency estimated based thereon.

In order to estimate the energy dissipation due mechanical vibrations,the machine may be treated as a set of driven damped harmonicoscillators. The acceleration may be defined as a(ω)=−a_(ω) sin(ωt),where a_(ω) is the acceleration amplitude per frequency. The work thatthe driving force performs is dW=Fdx; F is related to the accelerationby F=ma and dx is the enforced displacement. The total displacement iscalculated by

X _(ω)(t)=∫₀ ^(t)∫₀ ^(t′)α_(ω)(t″)dt″dt′

Substituting the acceleration in the above expression yields

${X_{\omega}(t)} = {{\int_{0}^{t}{\int_{0}^{t^{\prime}}{{- a_{\omega}}{\sin \left( {\omega \; t^{''}} \right)}{dt}^{''}{dt}^{\prime}}}} = {\int_{0}^{t}{\frac{a_{\omega}}{\omega}{\cos \left( {\omega \; t^{\prime}} \right)}{dt}^{\prime}}}}$

The displacement differential is therefore

${dx}_{\omega} = {\frac{a_{\omega}}{\omega}{\cos \left( {\omega \; t} \right)}{dt}}$

and the energy density per frequency is

${dW}_{\omega} = {{Fdx}_{\omega} = {{{ma}_{\omega}{dx}_{\omega}} = {{{- m}\frac{a_{\omega}^{2}}{\omega}{\cos \left( {\omega \; t} \right)}{\sin \left( {\omega \; t} \right)}{dt}} = {{- m}\frac{a_{\omega}^{2}}{2\omega}{\sin \left( {2\omega \; t} \right)}{dt}}}}}$

Thus the dissipated power density due to mechanical vibrations is

${P_{vib}(t)} = {\frac{{dW}_{\omega}}{dt} = {{- m}\frac{a_{\omega}^{2}}{2\omega}{\sin \left( {2\omega \; t} \right)}}}$

Summing all frequencies yields

${P_{vib}(t)} = {- {\sum\limits_{\omega}{m\frac{a_{\omega}^{2}}{2\omega}{\sin \left( {2\omega \; t} \right)}}}}$

It is noted that the power oscillates at twice the phase of theoscillator since every period has two cycles of energy absorption anddissipation. The average vibration power per frequency may be calculatedaccording to:

${\langle{P_{vib}(\omega)}\rangle} = {{m\frac{a_{\omega}^{2}}{2\omega}\frac{4}{T}{\int_{0}^{T\text{/}4}{{\sin \left( {2\omega \; t} \right)}{dt}}}} = {{m\frac{a_{\omega}^{2}}{2\omega}\frac{2\omega}{\pi}\left( \frac{- {\cos \left( {2\omega \; t} \right)}}{2\omega} \right)_{0}^{\pi \text{/}2\omega}} = \frac{{ma}_{\omega}^{2}}{\pi\omega}}}$

The total dissipated power may be calculated by summing the measuredfrequency components in the acceleration spectrum for each axis

${\langle P_{vib}\rangle} = {\sum\limits_{i = {xyz}}{\sum\limits_{\omega}{m\frac{a_{\omega}^{2}(i)}{\pi\omega}}}}$

With regards to magnetic energy loss, it is noted that most of thedriven power in the motor is translated into magnetic energy, whichmagnetic energy in turn generates currents in the rotor bars. Thesecurrents interact with the magnetic field by the Lorenz force causingthe rotor to rotate. There are three mechanisms of energy dissipationdue to magnetic fields, namely eddy currents heating, hysteresisheating, and wasted potential energy of the stray fields.

The first two of these dissipation mechanisms may be accounted for bythe output of a temperature sensor, as further detailed hereinbelow.With regards to the energy loss by the stray fields, it is noted thatthe potential energy stored in a magnetic field is:

$E = {\frac{1}{2\mu}{\int{B^{2}{dV}}}}$

The volume element in cylindrical coordinates is dv=rdθdrdz leading to:

$E = {\frac{1}{2\mu}{\int_{r_{motor}}^{\infty}{B^{2}{rdr}{\int_{0}^{2\pi}{d\; \theta {\int_{0}^{L}{dz}}}}}}}$

The field at the motor shielding may be expressed as:

$B = {{\frac{\mu \; I}{4\pi}{\int\frac{\overset{\rightarrow}{dl} \times \hat{r}}{r^{2}}}} = \frac{\overset{\rightarrow}{A_{0}}}{r^{2}}}$

where A₀ is the magnetic field amplitude.

In this treatment, it is assumed that the generated current flows in along wire with a vector dl^(˜) orthogonal to the machine radius.Furthermore, it is assumed that r_(det)−r_(motor)

r_(motor) where r_(det)=r_(motor)+h and h is the height of the sensor,r_(det) is the motor distance from the sensor to the center of the motorand r_(motor) is the radius of the motor. Under these assumptions A₀ isfixed and the measured field in the detector is B(r_(det))=A₀/r_(det) ²leading to A₀=B(r_(det))·r_(det) ². Thus, the expression for themagnetic field at any radius is B(r)=B_(det)·r_(det) ²/r². SubstitutingB(r) in the expression for the total energy:

$E = {{\frac{1}{2\mu}{B_{\det}^{2} \cdot r_{\det}^{4}}{\int_{r_{motor}}^{\infty}{\frac{1}{r^{3}}{dr}{\int_{0}^{2\pi}{d\; \theta {\int_{0}^{L}{dz}}}}}}} = \frac{\pi \; {{LB}_{\det}^{2} \cdot r_{\det}^{4}}}{2\mu \; r_{motor}^{2}}}$

Since the magnetic field is frequency dependent, B(ω)=B₀ sin(ωt).Therefore, the magnetic dissipated power is:

$P_{\omega} = {\frac{dE}{dt} = {{\frac{\pi \; {{LB}_{\det}^{2} \cdot r_{\det}^{4}}}{4\mu \; r_{motor}^{2}} \cdot \omega}\; {\sin \left( {2\omega \; t} \right)}}}$

and the averaged dissipated power is given by:

${\langle P_{magnetic}\rangle} = {\sum\limits_{\omega}\frac{\omega \; {{LB}_{\det}^{2} \cdot r_{\det}^{4}}}{2\mu \; r_{motor}^{2}}}$

With regards to power dissipation due to heat loss, it is noted thatduring normal machine operation, when no mechanical or electricalproblems are present, the dissipated power due to heat loss should bethe dominant power dissipation mechanism. The various mechanisms of heattransfer, specifically heat conduction, thermal radiation, and heatconvention may be modeled.

Heat conduction may be calculated using Fourier's law and may beparticularly useful for evaluating the thermal conduction in the solidparts of the motor according to

$P_{conduction} = {{{kA}\frac{dT}{dx}} = {k\frac{A}{L}\left( {T_{rotor} - T_{room}} \right)}}$

where k is the thermal conductivity and A and L are the conduction areaand length respectively. Although the rotor temperature is not measureddirectly, the sensor temperature may be used as a lower limit thereof.

The heat convection may be calculated using Newton's law and may beuseful for evaluating the air convection by the motor fan

Pconvection=hA(Tmachine·Troom)

where h is the convection coefficient. An accurate calculation of hrequires the detailed structure of the fan and the geometry of the innerparts of the motor to be known. In order to overcome this requirement, adedicated table with various values of h according to the motordimensions and rpm may be provided.

For the thermal radiation emitted from the motor shielding/stator theStephan Boltzmann law may be used:

P _(radiation) =ϵσAT _(machine) ⁴

where ϵ is the emissivity and σ is the Stephan Boltzmann constant.

In accordance with another preferred embodiment of the presentinvention, in the case of a motor, by way of example, the rotation ofthe motor is based on converting the electrical current I_(in) into arotating magnetic field B(t), which in turn induces current in the rotorbars I_(rotor) The rotor current is coupled with the magnetic field bythe Lorentz force F(t)=I_(rotor) L×B(t) leading to the rotation of therotor. The rotor speed frequency f_(r) must be lower than the magneticfield rotation frequency f_(b), in order for current to be induced inthe system. The relative difference in these frequencies is defined asthe slip of the system:

$s = \frac{f_{b} - f_{r}}{f_{b}}$

f_(r), f_(b) may be calculated from the vibration and magnetic spectra,respectively. In the case of adding a load to the system, as whenequipment is coupled to the motor, the slip is further increased. Sincethe slip is proportional to the load of the machine, the slip is alsoproportional to the power consumed by the motor. By calculating themagnetic energy which is proportional to the incoming power, the machineefficiency may be estimated.

It is appreciated that the various machine conditions, includingelectrical and mechanical faults described hereinabove with reference toFIGS. 6-12, as relating to asynchronous electrical machines, and withreference to FIGS. 14-17, as relating to synchronous electricalmachines, are provided by way of example only. Systems of the presentinvention, such as systems 100, 400 and 1300 may additionally oralternatively be used in the identification of a wide range ofmechanical and electrical faults of synchronous and asynchronouselectrical machines.

Furthermore, it is understood that although systems of the presentinvention may advantageously allow the performance of phase analysis ondata sensed thereby, due to the synchronous sampling of operationalparameters by a plurality of sensors, such phase analysis is notnecessarily performed by systems of the present invention.

Reference is now made to FIG. 20, which is a simplified illustration ofa system for automated monitoring of a machine, constructed andoperative in accordance with another preferred embodiment of the presentinvention.

As seen in FIG. 20, there is provided a system 2000 for identifyingpotential failures and providing pre-failure alerts for at least onemachine having at least one shared mechanical or electricalcharacteristic with a plurality of machines. System 2000 preferablyincludes a plurality of operational parameter sensing modules, such asoperational parameter sensing modules 2002, associated with a pluralityof machines having at least one common mechanical or electrical feature,such as mechanical machines 104, here embodied by way of example aspumps.

It is appreciated, however, that plurality of machines 104 mayalternatively comprise a plurality of electrical machines, whichelectrical machines may be synchronous or asynchronous electricalmachines including motors or generators. Alternatively, plurality ofmachines 2004 may include a combination of mechanical and electricalmachines, which mechanical and electrical machines may beinterconnected, such as a generator connected to an engine or a motorconnected to a pump.

Plurality of machines 2004 may include two or more machines, hereillustrated, for the sake of simplicity, as comprising only twomachines. Plurality of machines 2004 preferably share at least onecommon mechanical or electrical feature, such as, by way of example, acommon mechanical structure (e.g. centrifugal pump), machine type (e.g.part number), environmental feature such as location (e.g. collocatedmachines), operating parameters or performance characteristics (such asload, temperature/humidity), operational purpose (e.g. machines workingon similar tasks or in parallel on the same task), similar or identicalconstituents (e.g. same or similar motor, pump).

Machines 2004 may be of the same type, as in the case of pumps 2004shown in FIG. 20. Alternatively, machines 2004 may be of differenttypes, provided that machines 2004 have at least one common mechanicalor electrical feature.

Each one of operational parameter sensing modules 2002 is preferablyrespectively associated with an individual one of pumps 2004. Each oneof operational parameter sensing modules 2002 is preferably configuredand operative to provide output indications of at least one operationalparameter of each of plurality of machines 2004. Particularlypreferably, each one of operational parameter sensing modules 2002 isoperative to provide historical output indications of at least changesover time in at least one operational parameter of each of plurality ofmachines 2004.

By way of example, operational parameter sensing modules 2002 mayprovide output indications of patterns of change over time in one ormore of machine temperature, vibrations, acoustic emissions, currents,voltages, magnetic or electromagnetic flux. Operational parametersensing modules 2002 preferably comprise one or more sensors forrespectively sensing the one or more operational parameters of themachine with which the sensing modules are associated. For example, inone preferred embodiment of the present invention, operational parametersensing modules 2002 may include a combination of some or all ofvibration, acoustic, ultrasonic, magnetic, electromagnetic, current andtemperature sensors.

Data relating to at least one operational parameter of each of pluralityof machines 2004, and particularly preferably data relating to changesover time in the at least one operational parameter of each of pluralityof mechanical machines 2004, as sensed by sensors of each of operationalparameter sensing modules 2002, is preferably collected by a pluralityof data collection modules 2006. Each data collection module 2006 mayform a part of a corresponding operational parameter sensing module2002. Alternatively, each data collection module 2006 may be provided asa distinct entity, separate from the operational parameter sensingmodule 2002 with which it is associated.

By way of example, operational parameter sensing module 2002 includingdata collection module 2006 may be embodied as an Auguscope™,commercially available from Augury Systems Ltd, the assignee of thepresent application. Alternatively, operational parameter sensing module2002 including data collection module 2006 may be embodied as sensormodule 110. Operational parameter sensing module 2002 may be installed,either permanently or temporarily on each one of mechanical machines2004.

Output indications relating to at least one operational parameter ofeach of plurality of machines 2004 and particularly preferablyhistorical output indications relating to changes over time in at leastone operational parameter of each of plurality of mechanical machines2004, as sensed by operational parameter sensing modules 2002 associatedwith each one of machines 2004, are preferably transmitted by acommunication module 2010 to a server 2012, typically on the cloud, forprocessing. Communication module 2010 may be incorporated within datacollection module 2006 or may be provided as a separate component. Forexample, communication module 2010 may be embodied as data processingmodule having communication functionality incorporated therein. Server2012 is particularly preferably embodied as server 160, and preferablyincludes algorithmic processing capabilities.

In order to reduce the quantity of data being transmitted to server2012, processing may initially be performed locally at data collectionmodule 2006 or at communication module 2010. Such local processing mayinclude comparing a current recording of an operational parameter withhistorical recordings of that operational parameter of machine 2004 andsending the data relating to the current recording to the server 2012only in case that the data relating to the current recording issignificantly different than historical recordings.

Historical data from all of the sensing modules 2002 is preferablycollected at the server 2012 for each machine 2004. At the server 2012,the data is analyzed by automatic software algorithms which generateresults and present the generated results to users using a visualizationmodule 2014, which may be a smartphone or a web application. In additionsystem 2000 may include audio output capabilities for sound playback2015.

In addition to processing of information in a cloud server as describedin U.S. Pat. No. 9,835,594, filed Oct. 22, 2012 and entitled AUTOMATICMECHANICAL SYSTEM DIAGNOSIS, the disclosure of which is herebyincorporated by reference, there is additionally provided, in accordancewith preferred embodiments of the present invention, an automaticalgorithm that analyzes historical data and events on the machines 2004and finds similar historical patterns. These types of patterns may bederived using Markov chains or similar algorithms.

More specifically, the processing of data in the cloud server 2012preferably includes correlating, by a correlator 2020, patterns ofchanges in the at least one operational parameter in ones of theplurality of machines to past failures in corresponding ones of saidplurality of machines and providing a correlation output indication.Analysis of repeating patterns in historical measurements betweenmachines and correlation of these measurements to machine failuresserves to provide valuable information in diagnosis of similar machinessharing mechanical or electrical characteristics with the measuredmachines.

In some embodiments of the present invention, results of automaticsoftware algorithms may be provided to local processing components suchas data collection module 2006 or communication module 2010, so as toallow correlating and predicting functionalities to be performedlocally, thus obviating or reducing the need for transfer of data toserver 2012.

System 2000 further preferably includes an operational parameter sensingmodule 2022 associated with a given machine 2024 having at least onemechanical or electrical feature in common with machines 2004, forproviding an individual output indication of at least one operationalparameter, and particularly preferably of at least a change over time inthe at least one operational parameter of the given machine 2024. Datarelating to changes over time in the at least one operational parameterof given machine 2024, as sensed by sensors of operational parametersensing module 2022, is preferably collected by a data collection module2026. The individual output indication from operational parametersensing module 2022 is preferably provided to server 2012 via acommunication module 2030. It is appreciated that operational parametersensing module 2022, data collection module 2026 and communicationmodule 2030, may be generally of the same type as operational parametersensor modules 2002, data collection modules 2006 and communicationmodules 2010. Particularly preferably, operational parameter sensingmodule 2022 is embodied as sensor module 110 and communication module2030 is embodied as data processing module 150 having communicationfunctionality incorporated therein.

The processing of information at server 2012 preferably additionallyincludes predicting functionality, by a predictor 2040, operative toreceive the correlation output indication from correlator 2020 and theindividual output indication from operational parameter sensing module2022 associated with given machine 2024. Predictor 2040 preferablyprovides a predictive output indication of an impending failure of givenmachine 2024 by applying the correlation output indication establishedbased on plurality of machines 2004 to the individual output indicationof given machine 2024, based on a similarity between the change overtime in the at least one operational parameter of the given machine 2024indicated by the individual output indication and the patterns ofchanges over time in the least one operational parameter of theplurality of machines 2004.

Visualization module 2014 may be embodied as a notification module, forproviding notification of a status of the given machine 2024 based onthe predictive output indication provided by predictor 2026. At leastone of control, repair or maintenance activities are preferablyperformed upon given machine 2024 in accordance with the notification.In one embodiment, the notification may be a human-sensible notificationand the control, repair or maintenance activities be manually orautomatically performed in response to and in accordance with thenotification. Additionally, in accordance with an embodiment of theinvention, the system described may feedback the output of predictor2040 to a controller of given machine 2024 in order to modify themachine operation.

In one possible embodiment, signals collected by sensing module 2022 areenhanced at server 2012 and played using audio playback capabilities atan audio module 2050. Such enhancement may be associated with the outputof the correlator 2020 or predictor 2040, such that at least onecharacteristic of the audio signal corresponding to the predictiveoutput indication of predictor 2040 is selectively enhanced. By way ofexample, sensing module 2022 may include a microphone or vibrationaccelerometer. Upon detection of mechanical or electrical malfunction bycorrelator 2020 and/or predictor 2040, such as, for example, a bearingfault, the signal features related to the malfunction may be selectivelyemphasized in the signal and an augmented signal played using audioplayback capabilities module 2050. The playback of an augmented signalby audio module 2050 is preferably performed in parallel to notificationand visualization of the signal at visualization module 2014.

Such enhancement may, by way of example, be generated by amplifyingsignal frequencies related to the mechanical or electrical malfunctionwhile suppressing all other frequencies. An augmented audio signal maysignificantly aid a human analyst in data analysis.

In accordance with one preferred embodiment of the present invention,data, such as magnetic or vibration signals, are predicted by apredictor module, such as a predictor module 2140 of FIGS. 21 and 22.The predicted data predicted by predictor module 2140 is subsequentlycorrelated by a correlator 2142 to actual measured data provided by atleast one sensor module such as sensor module 2022. Based on thecorrelation, previously known signal components may be removed and newsignal components, associated with a developing fault, enhanced at anaudio playback module 2144. Such audio enhancement may be applied to anindividual recording, as illustrated in FIG. 21, or to a continuousrecording, as illustrated in FIG. 22.

Given machine 2024 is illustrated here as being outside of the group ofhistorically monitored machines 2004, in order to distinguish givenmachine 2024 therefrom. Given machine 2024 may indeed be outside of thegroup of historically monitored machines 2004. Impending failure ofgiven machine 2024 may be diagnosed by applying a correlation to datacollected from given machine 2024, which correlation has beenestablished based on historical patterns in data collected from group ofmachines 2004 having at least one mechanical or electricalcharacteristic in common with given machine 2024. Alternatively, givenmachine 2024 may be included in the group of historically monitoredmachines 2004. In this case, given machine 2024 may contribute datarelating to historical changes in at least one operational parameter,based on which data relating to historical changes a correlation may beestablished and then applied to given machine 2024.

It is appreciated that system 400 described hereinabove with referenceto FIG. 4 may be considered to be one possible implementation of system2000. In the case that system 2000 is implemented as a crowd-sourcingsystem as shown in FIG. 4, plurality of machines 2004 may includeplurality of electrical machines 402 having at least one sharedelectrical characteristic. Operational parameter sensing modules 2002may include a plurality of magnetic sensors, such as magnetic sensors130 in sensor modules 110, coupled to the corresponding plurality ofelectrical machines 402 having at least one shared characteristic forsensing magnetic fields generated thereby, the plurality of magneticsensors 130 preferably providing output indications of the magneticfields of the corresponding plurality of electrical machines 402.Plurality of magnetic sensors 130 are preferably included in sensormodules 110, operating synchronously as described hereinabove.Operational parameter sensing modules 2002 may additionally include aplurality of vibration sensors 140 operating synchronously withplurality of magnetic sensors 130.

Processing at data collection module 2006 and/or cloud server 2012 mayinclude correlating functionality, wherein the output indications of themagnetic fields of the corresponding plurality of electrical machines402 are received at correlator 2020 and a correlation output indicationof a correlation between the magnetic fields and past failures ofcorresponding ones of the plurality of electrical machines 402 isprovided.

Operational parameter sensing module 2022 associated with given machine2024 having at least one mechanical or electrical feature in common withmachines 402 may include at least one magnetic sensor 130 associatedwith given electrical machine 2024 having the at least one sharedcharacteristic for providing an individual output indication of magneticfields generated by the given electrical machine. Operational parametersensing module 2022 associated with given machine 2024 may additionallyinclude at least one vibration sensor 140 operating synchronously withthe at least one magnetic sensor 130 in sensor module 110.

Processing at data collection module 2006 and/or cloud server 2012 mayfurther include predicting functionality, wherein the correlation outputindication and the individual output indication are received bypredictor 2040 and a predictive output indication is provided. Thepredictive output indication may include an indication of an impendingfault, the impending fault comprising at least one of a crawling fault,eccentricity, a damaged rotor bar, a stator short, electrical discharge,mechanical imbalance, energy loss, negative phase sequence and faultsarising from extremum operating conditions, as detailed hereinabove withreference to FIG. 4. The predictive output indication may additionallyor alternatively include a prediction of time to failure of the givenelectrical machine, based on applying the correlation output indicationto the individual output indication.

Prediction of time to failure, based on historical changes inoperational parameters, may be better understood with reference to FIG.23.

Reference is now made to FIG. 23, which is a simplified graphicalpresentation of patterns of change in operational parameters of amechanical or electrical machine prior to machine failure.

As seen in FIG. 23, operational parameters A, B, and C may be collectedfrom one or more than one machine. By way of example, for wide-bandsignals such as vibration, acoustic, ultrasonic, magnetic andelectromagnetic signals, parameters A, B, and C may be energy atspecific frequencies or energy in predefined frequency bands. Fornarrow-band signals, such as temperature, humidity, or concentration ofspecific particulates in the air, parameters A, B and C may correspondto an average value over a specified time period or an exponentiallyweighted average or any higher level moments such as variance orskewness. For images, such as thermographic images for example,parameters may be average RGB levels of the pixels related to aparticular component. Parameters A, B and C may be collectedsynchronously for a particular machine or non-synchronously for aparticular machine. Parameters A, B and C may also be collectedsynchronously for a group of machines having a common mechanical featureor non-synchronously for a group of machines having a common mechanicalfeature. Particularly preferably, parameters A, B and C may be collectedby sensor module 110 in communication with data processing module 150and cloud server 160, as described hereinabove with reference to FIGS.1-5.

These operational parameters may be measured and values thereofcalculated based on data collected from the one or more machines.Various data handling methods may be applied to the data collectedincluding various different types of transformations such as, forexample, derivatives of smoothed raw data, measurement of overallvibrations at various ranges of frequencies, values from FFT calculatedspectra and the derivatives thereof and others.

Diagnosis of the operation of a machine may be based on patterns ofchange in machine parameters A, B and C over time. Patterns of change inmachine operational parameters may be characterized by a start of asequence of events, an order of successive events, time intervalsbetween successive events and an end of the sequence of events. Eachevent is preferably characterized by a condition that triggered theevent and the duration of the event and is reflected as a pattern ofchange over time of a particular parameter. For example, referring toFIG. 23, a rise in parameter A is the start event of a pattern. Rise inparameter A is followed by a decrease in parameter C after time t₁,followed by a fast rise in parameter C with duration of t₂, and so on.

The end event of the pattern is a failure of a specific machinecomponent, indicated as ‘machine failure’ in FIG. 23, following timet_(n).

Event duration and time intervals between successive events mayadditionally or alternatively be measured in machine cycles or number ofrotations performed by the machine. The x-axis of FIG. 23 may thereforebe replaced by units of number of machine cycles rather than time.

The patterns leading to machine failure are flexible in time and areinfluenced by load, machine usage patterns, operating conditions andother factors. For example, rate of fault development in continuouslyworking machines with a high load is higher than the rate of faultdevelopment in the same or similar machines with a low load. The patternof events preceding machine failure for a machine working under highload will therefore span a shorter time compared to the pattern ofevents preceding machine failure for a machine working under a low load.

Characteristic failure deterioration rate may be roughly estimated basedon operating parameters and fine-tuned based on the timing of the chainof deterioration event patterns. Such event patterns may beautomatically extracted from the historical data collected from theplurality of machines 402 or 2004 during a learning stage, using machinelearning algorithms. The extracted patterns may be used to characterizedevelopment of specific machinery faults.

A learning process to characterize development of specific machineryfaults preferably includes detection of significant events in multipleparameters and correlation between significant events to known componentfailure. Detection of significant events is preferably performed onhistorical data obtained from plurality of machines 402 or 2004 andpreferably includes detection of irregular values of operationalparameters. Such irregular values may include local maximums orminimums.

Correlation between significant events is preferably performedautomatically based on historical data using known algorithms. Forexample, correlator 2020 may use one of the Markov models such as HiddenMarkov chains for process modeling. Maximum-likelihood, Bayesianinterference and other approaches may be used for learning a model fromdata.

During an evaluation stage, the system preferably correlates datareceived from given machine 2024 and the historical data to one of thelearned patterns. In a case that significant correlation is found, thesystem preferably provides a probability that the specific patternindeed exists in the given machine 2024 and gives an indication ofestimated time to failure for a specific fault. It is understood thatsuch learning and evaluation processes may be confined to processingalgorithms within server 160 or 2012 or may be at least partiallyperformed by data processing module 150 or data collection module 2006.

Data relating to patterns of change in an operational parameterpreceding failure of a monitored machine are displayed in FIG. 24. Inthis case, the machine being monitored was an exhaust fan and themonitored operational parameter was energy of the peak at a first marker2401, energy of a band around a second marker 2402 and energy of theband around a third marker 2403. Graphs A-D display data respectivelyobtained over four immediately successive time intervals spanningseveral months, with graph A showing data for the earliest recording,obtained during normal machine operation, and graph D showing data forthe latest recording, taken several days before machine failure.

As is readily appreciated from a comparison of the spectra of graphsA-D, the spectra obtained from the machine are seen to changesignificantly over time, in the lead up to machine failure.

Changes in three major operating parameters preceding the failure of thesame machine for which data is shown in FIG. 24, are charted in FIG. 25.Data presented in FIG. 25 corresponds to a smoothed version of severaldata recordings collected over time, including the data displayed inFIG. 24.

In this case, the event pattern associated with failure of the machinestarts at time point 3, when there is a significant rise in value ofparameter 1A and significant decrease in value of parameter 2A. This isfollowed by a gradual rise in parameter 3A, concurrent with anadditional rise in parameter 1A a few days before failure.

In a preferred embodiment of the present invention, patterns of changein parameters monitored by various sensors may automatically becombined. By way of example, patterns of change of multiple operationalparameters may be measured by a continuous monitoring platform of thetype of system 100, 400 or 1300, including tri-axial synchronousmeasurement of vibrations as well as temperature and magnetic sensing.Systems well-suited for such continuous monitoring include electricalmotors and generators, transmission and driven equipment.

In one exemplary data-collection set-up carried out by the presentinventors, a continuous monitoring platform of the type shown in system100 was used to monitor electrical motors and driven equipment. Sensorswere installed on two motor locations near to the motor bearings and onthe driven equipment near to the equipment bearings.

The process of bearing deterioration was found to be as follows:

Initially, significant changes in the magnetic field of the motor incomparison to historical data were detected at one of the motorlocations. Changes in the magnetic field were sensed by magnetic sensorsof a type resembling magnetic sensor 130 in sensor module 110. Thisfailure is believed to be related to the development of faults in motorelectrical circuits such as, for example, cracked rotor bars.Subsequently, further development of electrical faults was found togenerate more severe changes in magnetic field. The non-symmetricmagnetic fields caused vibrations of the rotor, which vibration levelswere found to increase over time as the fault progressed. Suchvibrations were recorded by vibration sensors of a type resemblingvibration sensors 220, 222, 224. High vibrations of the rotor generatedhigher load on motor bearings and as a result caused acceleratedmaterial fatigue of the bearings. At early stages of development ofbearing failure the indications were primarily available in very highvibrational frequencies and in rise of energies in demodulated spectra.Progressive bearing failure generated energy that was found to becomevisible at lower frequencies. Advanced bearing failures caused a rise intemperature of the bearings and were recorded by a temperature sensorincluded in sensor module 110.

The following parameters may be related to the above-described chain ofevents:

(1) Magnetic field of motor relative to historical baseline of a givenelectrical machine or other similar electrical machines operating undersimilar operating conditions;(2) Total energy in high frequency spectra relative to historicalbaseline of given machine or other similar machines;(3) Total energy in demodulated spectra relative to historical baselineof a given machine or other similar machines;(4) Non-synchronous energy relative to historical baseline of a givenmachine or other similar machines;(5) Bearing temperature relative to historical baseline of a givenmachine or other similar machines.

The above-described chain of events is indicated by a rise in parameters(1) to (5) in sequential order with time. Machine learning algorithmsmay be provided with the patterns of change of these historicalparameters and may be used to automatically extract that sequence (1) to(5) will ultimately result in machine failure. Such machine learningalgorithms may be executed by server 160 and/or data processing module150.

The input of machine learning algorithms is a normalized set ofparameters as described herein above and the desired output may be, forexample, predicted time-to-failure. Training of such machine learningalgorithms is performed by providing historical examples of bearingfailures. During an evaluation stage, each time data is recorded fromthe sensors, parameters (1)-(5) are calculated on the data. During atraining stage these and other parameters are calculated usinghistorical data as the input to the algorithm and time-to-failureprovide as a target output.

Reference is now made to FIG. 26, which is a simplified illustration ofa portion of system for automatic monitoring and control of a machine,constructed and operative in accordance with another preferredembodiment of the present invention.

In accordance with a preferred embodiment of the present invention,there is preferably provided a plurality of operational parametersensing modules associated with a plurality of electrical or mechanicalmachines having at least one common electrical or mechanical feature,the plurality of operational parameter sensing modules providinghistorical output indications of at least one operational parameter ofeach of the plurality of mechanical devices over time, as illustrated inFIG. 20. The system preferably additionally includes a correlator, suchas correlator 2020 shown in FIG. 20, operative to correlate at least oneoperational parameter in ones of the plurality of machines to at leastone optimization metric in corresponding ones of the plurality ofmachines and to provide a correlator output.

Additionally, the system preferably includes an operational parametersensing module associated with a given machine having the at least onecommon electrical or mechanical feature for providing an individualoutput indication of the at least one operational parameter of the givenmachine and a control output generator operative to receive thecorrelator output and the individual output indication, for providing acontrol output useful for enabling the given machine to operate inaccordance with an operational parameter which is correlated by thecorrelator to have a desired optimization metric value.

FIG. 26 illustrates a portion of a system 2600 constructed and operativein accordance with this embodiment of the present invention. As isappreciated from consideration of FIG. 26, only a given machine to becontrolled by the system is shown, denoted Machine 1, and the pluralityof machines, based on the performance of which plurality of machines thecontrol output is generated, are denoted as Machines 2-N. The pluralityof machines 2-N and the components associated therewith are generally asshown in FIG. 20. Given machine 1 and machines 2-N preferably share atleast one common electrical or mechanical feature.

As seen in FIG. 26, an operational parameter sensor module 2622 ispreferably associated with a given machine 2624, for providing anindividual output indication of at least one operational parameter ofmachine 2624. By way of example, machine 2624 may be a mechanical orelectrical machine and is here illustrated to comprise a pump.Operational parameter sensor module 2622 may be any type of sensormodule suitable for monitoring operational parameters of machine 2624.Operational parameter sensor module 2622 is particularly preferablyembodied as one or more sensor modules 110 including a plurality ofsensors, preferably although not necessarily operating synchronously. Byway of example, operational parameter sensor module 2622 may at leastinclude magnetic sensor 130 and three tri-axial vibration sensors 220,222 and 224 preferably operating mutually synchronously.

The individual output indication from operational parameter sensingmodule 2622 is preferably provided to a data processing module 2626.Data processing module 2626 may be embodied as data processing module150, by way of example only. Data processing module 2626 may processdata received thereat relating to the sensed operational parameter ofmachine 2624. Particularly preferably, data processing module 2626 mayanalyze the individual output indication in accordance with any of theautomatic algorithms described hereinabove, in order to derive acondition of machine 2624. By way of example, data processing module2626 may detect impending failure of machine 2624 based on the conditionthereof, as sensed by operational parameter sensing module 2622.

Upon detection of impending failure, data processing module 2626preferably sends a signal to a control module 2628 interfacing with amachine controller and limits functionality of the machine 2624 in orderto prevent rapid deterioration of machine 2624. For example, highoverall vibration levels are a reliable indicator of inefficientmachinery performance and possible failure development. By alteringmachine operation, for example by reducing the load on machine 2624,machine vibrations may be correspondingly reduced and furtherdevelopment of failure thereby halted or delayed.

Changes in machine operation may be reported by a communication module2630 to main server 2012 by way of a communication router 2632 or to alocal node such as data collection module 2626, so as to alertmaintenance staff on limited system performance. Maintenance staff maymanually override this behavior using one of the system interfaces, suchas email, a smartphone application or web application.

In accordance with embodiments of the present invention, the system ofFIG. 26 may be used to examine trends in given machine 2624 and acontrol output may be fed to the machine 2624 in order to cause themachine to operate so as to realize a desired optimization metric value.The desired optimization metric may be machine efficiency, machine powerconsumption, machine vibration levels or estimated time of failure. Inthis case, operational parameter sensor module 2622 may sense one ormore optimization metrics or one or more optimization metrics may beobtained from external sources, such as electricity usage, maintenancerecords etc.

System performance may be optimized, by way of example, based onestimated time-to-failure. By way of example, for each machine beingcontinuously monitored, a threshold may be set for action based on knownavailability and repairs scheduling. For example, a machine repair cyclemay be scheduled for 3 months (T_(r)=90 days). Using systems asdescribed above in reference to FIGS. 20-25, time-to-failure({circumflex over (T)}_(i)) on each machine 1-N may be calculated eachtime a recording is performed. If the calculated time-to-failure crossesa repair cycle threshold or approaches this threshold such that it willcross the threshold before the next repair cycle and additional machinesare available for performing the same task as is performed by thefailing machine, control module 2628 may be used to change machineoperation in order to maximize minimal time-to-failure on all machinessuch that

∀t:min{circumflex over (T)} _(i)(t)>T _(r)

For example, two pumps may be connected to the same line. Based onvibration levels one of the pumps is expected to reach a dangerous statein 30 days. The other pump, based on vibration levels, is expected toreach that state in 200 days. Based on these estimations, the system maytransfer majority of the load to the healthier pump using control module2628 such that total machine availability remains high. At the sametime, an alert may be generated to maintenance staff to prepare for pumpmaintenance.

A control output from control module 2628 may be an alert, arecommendation, an alarm or indication to machine operator, instead ofor in addition to being an output, which directly causes a given machineto operate in a calculated manner.

System 2600 may additionally include an independent parameter sensormodule 2650. Data processing module 2606 may collect additionalinformation about the operating conditions of the machine using inputfrom independent parameter sensor module 2650 and diagnostic patternsmay be generated as described hereinabove with reference to FIG. 20and/or control outputs delivered based on these parameters. Suchparameters may include, for example, outside temperature, humidity,density of particulates in the air and others. Independent parametersensor module 2650 may form a part of a separate supporting system ormay be a dedicated entity in system 2600. Alternatively, parameterssupplied by independent parameter sensor module 2650 may be calculatedor estimated based on other additional data.

Additional information such as global operating parameters may also becollected by the main server 2012 or by other components in system 2000or 2600 such as communication module 2630. Such global information maybe a date, month, day of the week, financial market information, TVschedule or any other information not directly related to machineoperation.

Based on these parameters, the system may predict future performance andload and thus optimize machine operations accordingly. By way ofexample, the number of visits to an emergency room in a hospital may bestatistically lower during weekends than on other days. HVAC (HeatVentilation Air Conditioning) machine performance may therefore beoptimized based on the expected number of visitors. Further by way ofexample, unusually high outside morning temperatures may lead to theprediction of high loads on cooling systems near opening hours andcooling may hence be activated before the opening occurs.

Further by way of example, patterns of water consumption may changesignificantly during major events having many spectators. Predictingutilities consumption may allow more efficient machine usage and loweroperational costs.

Additionally in accordance with another preferred embodiment of thepresent invention, the system as described hereinabove with reference toFIGS. 20-26 may be configured and operative to sense control inputsprovided to the machine 2624, collect data from machine 2624 andcorrelate control inputs and data with other similar machines over timesuch as machines 2-N, optionally including machine 2624 itself. Thesystem may learn and/or establish correlations between control inputsand data. Based on these correlations, the system may identify anomalouscontrol inputs and/or system performance and alert maintenance staffaccordingly. Such anomalies may be due to errors in machine operation orsignificant changes in machine control procedure, for example as aresult of malicious software or invalid usage.

In the case that system 2000 of FIG. 20 and/or system 2600 isimplemented in order to automatically sense problematic conditions inmachine systems due to external malicious intervention, correlator 2020is preferably operative to correlate the at least one operationalparameter in ones of the plurality of machine systems 1-N to at leastone other parameter in ones of the plurality of machine systems 1-N andprovide a correlation output indication. The at least one otherparameter may or may not be a mechanical or electrical parameter.

Operational parameter sensing module 2022 associated with a givenmachine having the at least one common mechanical or electrical featurepreferably provides an individual output indication of the at least oneof said operational parameter and the other parameter of the givenmachine.

In this case, control module 2628 and communication module 2630 ofsystem 2600 may operate as an anomaly alert generator operative toreceive the correlation output indication and the individual outputindication and to provide an anomaly alert based on a dissimilaritybetween at least one of the operational parameter and the otherparameter of the given machine indicated by the individual outputindication and at least one of said operational parameter and the otherparameter indicated by the historical output indications.

Additionally or alternatively, control module 2628 may operate as acontrol output generator operative to receive the correlation outputindication and the individual output indication and preferably providinga hacking responsive control output to the given machine based on adissimilarity between at least one of the operational parameter and theother parameter of the given machine indicated by the individual outputindication and at least one of the operational parameter and the otherparameter indicated by the historical output indications.

Based on historical data collected for a given machine or machines atsimilar locations it is possible to create a model of machine operation.For example, in the case of a chiller it is possible to generate a modelbased on outside temperature, chiller power consumption and chiller loadthat will predict inside temperature, according to:

{circumflex over (t)} _(ins) =f(t _(out) ,P,L)

where t_(ins) is temperature inside the building, t_(out) is thetemperature outside, P is power consumption and L is chiller load. Ifsignificant deviations are found to exist between predicted temperatureand measured temperature, an alert may be generated. To find significantdeviations an estimation error may be calculated such as:

e(t)=t _(ins)(t)−{circumflex over (t)} _(ins)(t)

where {circumflex over (t)}_(ins) is the actual temperature inside thebuilding. The calculated errors may then be compared to errordistributions known from historical data and reflecting inherent modelaccuracy. Such a comparison may be made using statistical tools such ashypothesis testing or Bayesian methods. Machine models used may bepurely statistical such as used in statistical process control (SPC),machine learning or any other suitable kind of anomaly detectionalgorithms.

In the case that the monitored machine is part of a system includingmany machine components, one machine in the system may be diagnosedbased on monitoring of at least one operational parameter associatedwith another machine in the same system. An exemplary system foranalysis of one electrical or mechanical machine within a machinesystem, based on monitoring of another electrical or mechanical machinewithin the same system is illustrated in FIG. 27.

As seen in FIG. 27, a machine system 2700 may comprise a chiller 2702connected to a first pump 2704 and a second pump 2706. Operatingparameters of first and second pumps 2704 and 2706 are preferablymonitored and the condition of each of first and second pumps 2704 and2706 is preferably ascertained by way of a monitoring system 2710preferably associated with each of first and second pumps 2704 and 2706.

Monitoring system 2710 may be embodied as of any of the monitoringsystem types described hereinabove with reference to FIGS. 1-4 and FIGS.20-26 and preferably includes at least one operating parameter sensormodule such as sensor module 110 or sensor module 2002 in communicationwith at least one data processing module such as data processing module150 or data processing module 2606. Monitoring systems 2710 arepreferably in communication with a server 2720, which server 2720 may beembodied as server 160 or server 2012, by way of example only.

It is appreciated that chiller 2702 is preferably not directly monitoredby monitoring system 2710. By way of example, failure of chiller 2702forming part of system 2700 may be diagnosed based on changes in atleast one operating parameter, such as changes in vibrations, arisingfrom one or both of first and second pumps 2704 and 2706. It isappreciated that the operating state of a particular electrical ormechanical machine within a machine system may thus be identifiedwithout necessarily directly obtaining data from that machine, by way ofmonitoring of a different machine cooperating with the machine bediagnosed. Further by way of example, a defect or failure of a pumpimpeller may be diagnosed based on monitoring operational parameterssuch as magnetic flux associated with a motor driving the pump.

A system for diagnosing a particular machine within a system comprisinga plurality of machines, such as system 2700, may include at least oneoperational parameter sensing module, such as operational parametersensing module 2002 of FIG. 20, providing historical output indicationsof at least one operational parameter of at least one machine. Forexample, the sensing module may be a vibration sensor providinghistorical output indications of vibrations arising from first andsecond pumps 2704 and 2706 in system 2700. Further by way of example,the sensing module may be embodied as sensor module 110 synchronouslysensing at least magnetic and vibration data from first and second pumps2704 and 2706.

The system may further include a correlator, such as correlator 2020 ofFIG. 20, for correlating the historical output indications of the atleast one operational parameter to historical indications of at leastone additional parameter associated with at least one other machine inthe system and providing a correlation output indication. By way ofexample, the correlator may correlate historical output indications ofvibrations arising from first and second pumps 2704, 2706 withcorresponding historical output indications of an operating state ofchiller 2702 connected to pumps 2704, 2706. The correlation outputindication provided by correlator 2020 may include a correlation betweenvibrations of the pumps 2704 and 2706 and operating states of thechiller 2702, possibly including vibrations associated with defectivechiller operation or vibrations associated with failure of the chiller.The correlation output may be based on historical data from system 2700only, denoted system 1, or from similar systems 2-N.

The system may further include an operational parameter sensing module,such as operational parameter sensing module 2022, associated with agiven machine having at least one mechanical or electrical feature,environmental feature or performance feature in common with the at leastone machine for which historical output indications were obtained. Theoperational parameter sensing module 2022 preferably provides anindividual output indication of the at least one operational parameterof the given machine. For example, the operational parameter sensingmodule may be a vibration sensor sensing vibrations generated by thesame or a similar pump to that for which the historical vibrations andcorrelation were obtained.

The system may further include a control output generator, such ascontrol module 2628, operative to receive the correlation outputindication and the individual output indication, for applying thecorrelation output indication to the individual output indication forderiving the additional parameter and providing a control output to thegiven machine or machine system based on the additional parameterderived. For example, control module 2628 may receive the sensedvibrations from pump 2704 and 2706 and apply thereto the correlationoutput indication correlating pump vibrations to the chiller state. Thecontrol output generator may thus derive the operating state of chiller2702 without directly measuring current operating parameters of chiller2702.

It will be appreciated by persons skilled in the art that the presentinvention is not limited by what has been particularly claimedhereinbelow. Rather, the scope of the invention includes variouscombinations and subcombinations of the features described hereinaboveas well as modifications and variations thereof as would occur topersons skilled in the art upon reading the forgoing description withreference to the drawings and which are not in the prior art.

1-10. (canceled)
 11. A system for continuously monitoring at least onemachine comprising: at least one magnetic sensor sensing magnetic fieldemission arising from at least one machine and outputting magnetic fieldemission signals corresponding to said magnetic field emission; at leastone vibration sensor sensing vibrations arising from said at least onemachine and outputting vibration signals corresponding to saidvibrations, said sensing of said vibrations being performedsynchronously with said sensing of said magnetic field emission; asignal analyzer receiving at least a portion of said magnetic fieldemission signals and said vibration signals and performing analysis ofsaid magnetic field emission signals with respect to said vibrationsignals, said signal analyzer providing an output based on saidanalysis, said output comprising at least an indication of a conditionof said at least one machine; and a control module receiving saidindication of said condition and initiating at least one of a repairevent on said at least one machine, an adjustment to a maintenanceschedule of said at least one machine and an adjustment to an operatingparameter of said at least one machine based on said indication, wherebyefficacy of said at least one machine is improved.
 12. A systemaccording to claim 11, wherein said analysis comprises phase analysis ofphases of said magnetic field emission signals and said vibrationsignals.
 13. A system according to claim 11, wherein said analysiscomprises machine-learning functionality for estimation andclassification of said condition of said at least one machine.
 14. Asystem according to claim 11, wherein said signal analyzer comprises atleast one data processing module in communication with said at least onemagnetic sensor and vibration sensor and a cloud processing server incommunication with said at least one data processing module. 15-17.(canceled)
 18. A system according to claim 11, wherein said at least onemachine comprises at least one of an electrical machine and a mechanicalmachine.
 19. (canceled)
 20. A system according to claim 18, wherein saidelectrical machine comprises at least one of a motor and a generator.21-121. (canceled)
 122. A method for continuously monitoring at leastone machine comprising: sensing magnetic field emission arising from atleast one machine and outputting magnetic field emission signalscorresponding to said magnetic field emission; sensing vibrationsarising from said at least one machine and outputting vibration signalscorresponding to said vibrations, said sensing of said vibrations beingperformed synchronously with said sensing of said magnetic fieldemission; receiving at least a portion of said magnetic field emissionsignals and said vibration signals and performing analysis of saidmagnetic field emission signals with respect to said vibration signals,and providing an output based on said analysis, said output comprisingat least an indication of a condition of said at least one machine; andreceiving said indication of said condition and initiating at least oneof a repair event on said at least one machine, an adjustment to amaintenance schedule of said at least one machine and an adjustment toan operating parameter of said at least one machine based on saidindication, whereby efficacy of said at least one machine is improved.123. A method according to claim 122, wherein said analysis comprisesphase analysis of phases of said magnetic field emission signals andsaid vibration signals.
 124. A method according to claim 122, whereinsaid analysis comprises machine-learning functionality for estimationand classification of said condition of said at least one machine. 125.A method according to claim 122, wherein said analysis is performed byat least one data processing module and a cloud processing server incommunication with said at least one data processing module. 126-128.(canceled)
 129. A method according to claim 122, wherein said at leastone machine comprises at least one of an electrical machine and amechanical machine.
 130. (canceled)
 131. A method according to claim129, wherein said electrical machine comprises at least one of a motorand a generator. 132-222. (canceled)
 223. A system according to claim11, wherein a sampling difference in time between sampling by said atleast one vibration sensor and sampling by said at least one magneticsensor is less than or equal to 0.01/F_(s) where F_(s) is a sensorsampling frequency of said vibration sensor and said magnetic sensor.224. A system according to claim 12, wherein said phase analysiscomprises generation of at least one of an orbit plot and a phase plot,wherein said phase plot a relative phase of said magnetic field emissionsignals and vibration signals is plotted against a sum of said magneticfield emission and vibration signals energies.
 225. A system accordingto claim 13, wherein said machine learning functionality comprisesstatistical learning of a probability density function of saidsynchronously sensed magnetic field emission signals and said vibrationsignals.
 226. A system according to claim 14, wherein said dataprocessing module is in operative control of said at least one magneticsensor and said at least one vibration sensor so as to synchronize saidsensing synchronously performed by said at least one magnetic sensor andsaid at least one vibration sensor.
 227. A method according to claim122, wherein a sampling difference in time between said sensing of saidvibrations and said sensing of said magnetic field emission is less thanor equal to 0.01/F_(s), where F_(s) is a sensor sampling frequency of avibration sensor and a magnetic sensor respectively operative to performsaid sensing of vibrations and said sensing of said magnetic fieldemission.
 228. A method according to claim 123, wherein said phaseanalysis comprises generating at least one of an orbit plot and a phaseplot, wherein said phase plot a relative phase of said magnetic fieldemission signals and vibration signals is plotted against a sum of saidmagnetic field emission and vibration signals energies.
 229. A methodaccording to claim 124, wherein said machine learning functionalitycomprises statistical learning of a probability density function of saidsynchronously sensed magnetic field emission signals and said vibrationsignals.
 230. A method according to claim 125, and also comprising saiddata processing module controlling said sensing of said magnetic fieldemission and said vibrations, so as to synchronize said synchronouslyperformed sensing.